Smart Contracts

• 1: Evidence Record Smart Contract
• 2: Pricing Model: Estimated Cost Impact Assessment
• 3: Pricing Model: Journalist Income Simulation (1 Year)
• 4: Smart Contract Pricing Model
• 5: Relevance Metric: Lippmann Influence (l)
• 6: Relevance Metric: Universal Dialogue Quality Assessment (UDQA)
• 7: Relevance Metric: Supply/Scarcity (s)
• 8: Simulation: Freelance Videographer (State Legislature)

1 - Evidence Record Smart Contract

Evidence Record Smart Contract

The EvidenceRecord smart contract represents a core component for recording cryptographic proofs of quotes within Youseddit’s distributed ledger infrastructure. It facilitates linking quotes to their original context (stored off-chain) while maintaining GDPR compliance. The broader YouSeddit concept involves creating a verifiable conversation chain on the ledger, where each initiator and response quote potentially receives its own entry, linking back to the previous turn in the conversation.

Contract Overview

This specific EvidenceRecord contract example focuses on recording evidence for a response quote, linking its hash (responseQuoteHash) to the hash of the full encrypted exchange (fullExchangeHash) stored off-chain (e.g., on IPFS via storagePointer).

Important Note on Chaining: To realize the full concept of a verifiable conversation chain, the YouSeddit system (potentially using variations or extensions of this contract, or managing links off-chain) must ensure:

1. Both initiator and response quotes ideally receive distinct ledger entries.
2. Each ledger entry (or its associated C2PA manifest) contains a verifiable reference (e.g., transaction hash) to the ledger entry of the immediately preceding quote in the conversation flow.
3. The C2PA manifest generated for any quote robustly encodes this backward link, allowing context traversal.

This contract provides the mechanism to anchor a quote (primarily the response) to its full context; the explicit on-ledger chaining requires further architectural definition or is managed via C2PA references generated off-chain.

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.17;

contract EvidenceRecord {
    // Struct to hold evidence record data
    struct Evidence {
        bytes32 fullExchangeHash;   // SHA-256 hash of the complete encrypted email exchange (initiator + response)
        string storagePointer;      // IPFS CID pointing to the encrypted exchange file
        address owner;              // Owner of the evidence record (typically the source/interviewee providing the response quote)
        uint256 timestamp;          // When the evidence was recorded
        bool isPublished;           // Whether this response quote is approved for publication
    }
    
    // Mapping from response quote hash to evidence record
    mapping(bytes32 => Evidence) public evidenceRecords;
    
    // Events
    event EvidenceRecorded(bytes32 indexed responseQuoteHash, bytes32 fullExchangeHash, string storagePointer, address owner);
    event QuotePublicationStatusChanged(bytes32 indexed responseQuoteHash, bool isPublished);
    
    /**
     * @dev Record a new evidence entry linking a response quote to the full exchange
     * @param responseQuoteHash Hash of the specific response quote text
     * @param fullExchangeHash Hash of the complete encrypted email exchange file
     * @param storagePointer IPFS CID pointing to the encrypted exchange file
     */
    function recordEvidence(
        bytes32 responseQuoteHash,
        bytes32 fullExchangeHash,
        string memory storagePointer
    ) public {
        // Ensure this response quote hash hasn't been recorded before
        require(evidenceRecords[responseQuoteHash].timestamp == 0, "This response quote hash is already recorded");

        // Create and store the evidence record
        evidenceRecords[responseQuoteHash] = Evidence({
            fullExchangeHash: fullExchangeHash,
            storagePointer: storagePointer,
            owner: msg.sender,
            timestamp: block.timestamp,
            isPublished: false
        });
        
        emit EvidenceRecorded(responseQuoteHash, fullExchangeHash, storagePointer, msg.sender);
    }
    
    /**
     * @dev Change the publication status of a response quote
     * @param responseQuoteHash Hash of the specific response quote text
     * @param publishedStatus New publication status
     */
    function setPublishedStatus(bytes32 responseQuoteHash, bool publishedStatus) public {
        // Ensure the response quote record exists
        require(evidenceRecords[responseQuoteHash].timestamp > 0, "Evidence record does not exist");

        // Ensure only the owner can change the status
        require(evidenceRecords[responseQuoteHash].owner == msg.sender, "Only the owner can change publication status");

        evidenceRecords[responseQuoteHash].isPublished = publishedStatus;

        emit QuotePublicationStatusChanged(responseQuoteHash, publishedStatus);
    }
    
    /**
     * @dev Retrieve evidence record for a response quote hash
     * @param responseQuoteHash Hash of the specific response quote text
     * @return The evidence record
     */
    function getEvidence(bytes32 responseQuoteHash) public view returns (
        bytes32 fullExchangeHash,
        string memory storagePointer,
        address owner,
        uint256 timestamp,
        bool isPublished
    ) {
        Evidence storage evidence = evidenceRecords[responseQuoteHash];

        // Ensure the response quote record exists
        require(evidence.timestamp > 0, "Evidence record does not exist");

        return (
            evidence.fullExchangeHash,
            evidence.storagePointer,
            evidence.owner,
            evidence.timestamp,
            evidence.isPublished
        );
    }
    
    /**
     * @dev Check if a response quote is approved for publication
     * @param responseQuoteHash Hash of the specific response quote text
     * @return Whether the response quote is published
     */
    function isQuotePublished(bytes32 responseQuoteHash) public view returns (bool) {
        // Ensure the response quote record exists
        require(evidenceRecords[responseQuoteHash].timestamp > 0, "Evidence record does not exist");

        return evidenceRecords[responseQuoteHash].isPublished;
    }
}

Key Features

Data Structure

The Evidence struct contains:

• fullExchangeHash: SHA-256 hash of the complete encrypted email exchange file (initiator + response)
• storagePointer: IPFS CID pointing to the encrypted exchange file
• owner: Address of the evidence record owner (typically the source/interviewee)
• timestamp: When the evidence was recorded on the distributed ledger
• isPublished: Whether this response quote is approved for publication

Core Functions

recordEvidence

Records an evidence entry for a quote (typically the response quote), linking its hash to the hash of the full encrypted email exchange and its IPFS location. To support chaining, the off-chain system calling this function must handle the recording of the preceding quote and ensure the appropriate backward link is included in the C2PA manifest.

function recordEvidence(
    bytes32 responseQuoteHash,
    bytes32 fullExchangeHash,
    string memory storagePointer
) public

• responseQuoteHash: SHA-256 hash of the specific plaintext response quote
• fullExchangeHash: SHA-256 hash of the complete encrypted email exchange file
• storagePointer: IPFS CID pointing to the encrypted exchange file

setPublishedStatus

Allows the owner (source/interviewee) to control whether a specific response quote is approved for publication.

function setPublishedStatus(bytes32 responseQuoteHash, bool publishedStatus) public

• responseQuoteHash: Hash of the specific response quote text
• publishedStatus: New publication status (true/false)

getEvidence

Retrieves the complete evidence record for a specific response quote hash.

function getEvidence(bytes32 responseQuoteHash) public view returns (
    bytes32 fullExchangeHash,
    string memory storagePointer,
    address owner,
    uint256 timestamp,
    bool isPublished
)

isSnippetPublished

Checks if a specific response quote is approved for publication.

function isQuotePublished(bytes32 responseQuoteHash) public view returns (bool)

Event Emissions

EvidenceRecorded

Emitted when a new evidence record is created.

event EvidenceRecorded(bytes32 indexed responseQuoteHash, bytes32 fullExchangeHash, string storagePointer, address owner)

SnippetPublicationStatusChanged

Emitted when the publication status of a response quote is changed.

event QuotePublicationStatusChanged(bytes32 indexed responseQuoteHash, bool isPublished)

Usage Flow

1. Record Each Turn (Conceptual Flow):

   • Turn 1 (Initiator): Journalist sends initiator quote. System processes it, records it on the ledger (obtaining TxID_Initiator1), and generates its C2PA manifest.
   • Turn 2 (Response): Source sends response quote. System processes the exchange, records the response quote using recordEvidence (obtaining TxID_Response1), and generates its C2PA manifest. Crucially, this C2PA manifest must include a verifiable link back to TxID_Initiator1. The recordEvidence call anchors the response to the full exchange context (fullExchangeHash, storagePointer).
   • Turn 3 (Initiator): Journalist sends another initiator quote. System records it (obtaining TxID_Initiator2) and generates its C2PA manifest, which must link back to TxID_Response1.
   • …and so on for the entire conversation.

2. Authorization Control: The source/interviewee (owner) uses setPublishedStatus to control publication authorization for their specific response quotes recorded via this contract.

3. Verification: Publishers/readers use the C2PA manifest of any quote. The manifest allows verification of the quote itself against its ledger entry (TxID_Current) and enables tracing the conversation backward by following the embedded link to the previous quote’s ledger entry (TxID_Previous). The getEvidence function can retrieve the full context hash and IPFS pointer associated with a specific recorded quote hash.

4. Publication Check: Media outlets use isQuotePublished for specific response quotes recorded via this contract.

5. Authorization Control: The source/interviewee (owner) can call setPublishedStatus to control whether their specific response quote is authorized for publication.

6. Verification: Publishers and readers can call getEvidence using the hash of a response quote to retrieve the evidence record, including the hash of the full exchange, which can be used (with appropriate access) to verify its authenticity and context.

7. Publication Check: Media outlets can call isQuotePublished to verify whether a specific response quote is authorized for publication by the source.

GDPR Compliance

This smart contract design supports GDPR compliance by:

• Storing only cryptographic hashes and metadata on-chain, not personal data
• Keeping the full exchange content encrypted and off-chain on IPFS
• Allowing content owners to control publication status
• Providing a verifiable chain of custody for the conversation (via C2PA manifests linking preceding ledger entries) without exposing sensitive content directly on-chain.

2 - Pricing Model: Estimated Cost Impact Assessment

Estimated Cost Impact Assessment

This document provides a hypothetical assessment of the potential financial impact on newspaper production if all quoted material were sourced through a system using the Youseddit Smart Contract Pricing Model.

Disclaimer: This is a high-level estimate based on numerous assumptions and simplified parameters derived from the conceptual model. Actual costs would vary significantly based on real-world usage patterns and finalized model parameters.

Assessment Steps

1. Estimate Quote Usage: Assumed average number of quotes per newspaper edition.
2. Estimate Quote Characteristics: Distributed quotes across Time, Category, and Relevance factors based on simplified assumptions.
3. Define Model Parameters: Used example parameters from the pricing model documentation, notably a Base Price of $3.
4. Calculate Average Quote Cost: Determined the price for different quote types using the formula (Price = BasePrice × F_time × F_category × F_relevance) and calculated a weighted average.
5. Estimate Total Cost: Multiplied the estimated average quote cost by the estimated quote volume.

Key Assumptions (Simplified Examples)

• Volume: 50 quotes/day (Daily paper), 100 quotes/week (Weekly paper).
• Base Price: $3.00.
• Characteristics Distribution:
  • Time: Primarily fresh (70%), some recent (20%), few older (10%).
  • Category: Mostly standard impact (70%), some high impact (30%).
  • Relevance (Combined): Mostly medium (60%), some low (30%), few high (10%).
• Multipliers: Used example values (e.g., F_time from 5.0 down to 0.5, F_category 1.0 or 1.5, F_relevance 0.7 to 2.0).

Estimated Results

• Average Cost Per Quote: Roughly estimated to be in the $12 - $18 range, using $15 as a working average for this assessment.
• Total Estimated Cost Impact:
  • Daily Newspaper: ~50 quotes/day * $15/quote ≈ $750 per day (Approx. $274,000 annually).
  • Weekly Newspaper: ~100 quotes/week * $15/quote ≈ $1,500 per week (Approx. $78,000 annually).

Conclusion

Based on these simplified assumptions and the conceptual model (with a $3 base price), universally applying this pricing model to all quotes would introduce a significant new operational cost for traditional newspaper production.

The actual financial impact is highly sensitive to:

• The finalized Base Price.
• The specific multipliers and weights chosen for Time, Category, and Relevance factors.
• The real-world distribution of quote characteristics used by publications.
• Potential offsetting factors like new revenue streams or reduced costs elsewhere.

Further analysis with refined parameters and real-world data is necessary for a more accurate assessment.

3 - Pricing Model: Journalist Income Simulation (1 Year)

Journalist Income Simulation (1 Year)

This document presents a hypothetical simulation modeling the potential supplementary income for a single journalist over 12 months using the Youseddit Smart Contract Pricing Model. Unlike the static assessment, this simulation considers the cumulative effect of adding new quotes each month and the price decay impacting revenue from older quotes.

Disclaimer: This simulation uses simplified assumptions for quote characteristics, price decay, and licensing patterns. Actual income depends heavily on real-world factors.

Simulation Assumptions

• Base New Quotes Added: 20 per month.
• Base Initial Quote Price (Avg): $15.00 per license (Based on BasePrice=$3, F_time=5.0, F_cat=1.0, F_rel=1.0).
• Journalist’s Share: 90%.
• Base Initial Revenue per License: $15.00 * 90% = $13.50.
• Price Decay Function: F_time(t) = max(0.5, 5.0 * e^(-0.05 * t)), where t is days since quote creation. The price multiplier decays relative to the initial multiplier (5.0).
• Base Licensing Pattern (Avg per Quote):
  • Month 1 (Avg Age ~15 days): 3 licenses purchased.
  • Month 2 (Avg Age ~45 days): 2 licenses purchased.
  • Month 3 onwards: 0 licenses purchased (Total 5 licenses per quote).
• Base Revenue Calculation:
  • Month 1 Revenue/License ≈ $13.50 * (F_time(15) / F_time(0)) ≈ $13.50 * (2.36 / 5.0) ≈ $6.37
  • Month 2 Revenue/License ≈ $13.50 * (F_time(45) / F_time(0)) ≈ $13.50 * (0.53 / 5.0) ≈ $1.43
• Seasonality / Event Adjustments:
  • Summer Dip (July, Aug): 50% reduction in new quotes added AND 50% reduction in licenses purchased.
  • Winter Holiday Dip (Dec): 30% reduction in new quotes added AND 30% reduction in licenses purchased.
  • Event Spikes (Mar, Apr, Oct, Nov): Base quote production, but 25% increase in licenses purchased.
• Assumed Base Monthly Salary: $5,417 (approx. $65k/year, for visualization).¹

Monthly Income Simulation

Conclusion

Under these revised simulation assumptions (including seasonality and event spikes):

• The journalist’s supplementary income fluctuates significantly, peaking during event spikes (~$550/month) and dropping during seasonal dips (~$110-125/month).
• The total estimated supplementary income over 12 months is approximately $4,800.

This dynamic simulation yields a lower annual income than the static “Medium Pickup” estimate ($16,200) because it accounts for price decay. The inclusion of seasonality further reduces the total compared to the non-seasonal simulation (~$5,200) due to the significant impact of the summer dip.

This highlights the sensitivity of income potential to:

• Timing of Licenses: How quickly quotes are licensed after publication significantly impacts revenue due to price decay.
• Decay Rate: A slower decay rate would increase income.
• Licensing Volume: More licenses per quote directly increase income.
• Initial Quote Value: Higher initial multipliers (from relevance, category) increase income.

4 - Smart Contract Pricing Model

Smart Contract Pricing Model

Guiding Principles

Youseddit’s pricing for verified quote pairs (linked initiator and response quotes) must be clear and simple for journalists and writers. While the exact formula remains internal, the principles guiding it should be easy to grasp. This document outlines those principles.

We base pricing on three factors:

1. Time: How recent is the response quote (and the exchange it belongs to)?
2. Category: What is the topic?
3. Relevance: How prominent is the source providing the response quote? What is the quality of the response? How scarce is this type of information?

See a mock newspaper example demonstrating how verified response quotes (linked to their initiator context) might appear in a publication.

Philosophy: Empowering Journalists

While the underlying mechanics involve several factors, the goal for the journalist is simple: focus on securing and verifying impactful quote exchanges. The YouSeddit pricing model is designed to just work behind the scenes, automatically assessing the multifaceted value of your verified quote pair based on its timeliness, topic, and relevance.

We handle the complexity so you don’t have to. The system aims to intelligently capture the market value derived from your journalistic instincts – whether that’s breaking news, securing a response from a key figure, uncovering a unique perspective, or providing deep insight within a verified exchange. The result should feel intuitive: valuable journalistic work in securing and verifying quote pairs translates directly into fair compensation through the smart contract, empowering you to pursue the next story.

1. Time

New information costs more, reflecting the economic principle of the diminishing marginal utility of information over time. Like news, quote pairs lose value as they age. Prices for the pair will decrease from a set maximum for fresh exchanges to a minimum for older ones.

Next Step: Define a clear formula for this price decay over time for the quote pair, including maximum and minimum values.

2. Category

Quote pairs are categorized by the topic of the exchange. Topics with broad impact or high current interest (e.g., global economics, AI breakthroughs) result in a higher price for the pair than niche or local topics, reflecting media theories like Agenda-Setting.

Next Step: Create a defined list of categories and their corresponding price tiers or multipliers for quote pairs.

3. Relevance (Influence / Lippmann)

The price reflects the source’s (the individual providing the response quote) potential influence on public perception, or what Walter Lippmann termed the “pseudo-environment”. Quote pairs featuring responses from figures who significantly shape this pseudo-environment within a given category command higher prices than those from less influential individuals.

Next Step: Develop a system to rank sources’ influence within categories to set price tiers for the quote pairs they are part of.

3b. Relevance (Dialogue Quality / UDQA)

A different approach assesses relevance based on the intrinsic quality of the response quote itself using a structured rubric like the Universal Dialogue Quality Assessment (UDQA) framework. This focuses on measurable aspects like responsiveness, clarity, helpfulness, accuracy, and safety within the context of the initiator-response exchange.

• Focus on Content: This view prioritizes the substance and construction of the response quote, independent of the source’s fame.
• Quantifiable Rubric: UDQA provides specific dimensions (Responsiveness, Clarity, Helpfulness, Accuracy, Safety) that can potentially be assessed, even automatically, to generate a quality score.

This perspective suggests pricing for the quote pair could reflect not just who the source is, but how well their response quote communicates and contributes value based on the UDQA criteria within the exchange.

Next Step: Develop and evaluate methods (potentially NLP-based) for reliably scoring response quotes against the UDQA dimensions within their exchange context.

Alternative Perspective: Inverse Relevance Pricing

An alternative viewpoint argues that source relevance should exert inverse pressure on the price of the quote pair. The strongest form suggests that exchanges involving less prominent individuals might hold unique value and should therefore be more accessible (i.e., lower priced).

• Discovery Value (Weak Ties): Less prominent sources often offer novel perspectives in their responses. Lowering the cost barrier for these quote pairs encourages journalists to uncover potentially groundbreaking insights from less central sources.
• Supply, Demand, and Saturation: Highly influential figures generate a vast volume of public statements. Consequently, the marginal value and demand for one additional verified quote pair involving such a figure may be lower compared to a rare, unique exchange with a less-covered source. Pricing could reflect this dynamic.
• Amplifying Diverse Voices: Pricing that favors less “relevant” sources could actively promote journalistic diversity, giving platform to underrepresented viewpoints and challenging dominant narratives shaped by the usual influential figures.
• Incentivizing Deeper Reporting: Making quote pairs involving less-known experts or individuals cheaper could incentivize journalists to move beyond readily available soundbites and pursue more in-depth, original exchanges.

This perspective suggests that while source influence is a factor, a pricing model for quote pairs could strategically reduce the cost associated with lower-ranked source relevance to foster a richer information ecosystem.

Next Step: Evaluate the strategic implications of both direct and inverse source relevance pricing models for quote pairs on the YouSeddit platform.

Synthesized Pricing Formula (Conceptual)

While defining precise metrics for qualitative aspects like “dialogic quality” is complex, we can structure a conceptual formula to integrate the discussed factors, similar in approach to Multi-Attribute Utility Theory (MAUT) used in decision analysis. This formula serves as a framework for further definition:

$$ \text{Price} = \text{BasePrice} \times F_{\text{time}}(t) \times F_{\text{category}}(c) \times F_{\text{relevance}}(l, b, s) $$

Where:

• BasePrice: A constant minimum price for any verified quote pair.
• F_time(t): A time decay function. Value starts high for new quote pairs (t=0, based on exchange time) and decreases over time t, approaching a minimum multiplier > 0.
  • Example: Exponential decay: $$ F_{\text{time}}(t) = \max(\text{mult}{\text{min}}, \text{mult}{\text{init}} \times e^{-\lambda t}) $$ (where \lambda is the decay rate)
  • Requires Definition: initial_multiplier, min_multiplier, decay_rate.
• F_category(c): A multiplier based on the quote pair’s topic category c. Higher impact categories yield higher multipliers.
  • Requires Definition: A mapping of categories c to specific multiplier values (>= 1).
• F_relevance(l, b, s): A composite function combining different relevance aspects:
  • l: Lippmann Influence metric (source’s prominence/impact).
  • b: UDQA Quality metric (response quote’s intrinsic dialogue quality score within the exchange).
  • s: Supply/Scarcity metric (inverse of quote pair availability/source saturation).
  • Conceptual Structure: $$ F_{\text{relevance}}(l, b, s) \approx (1 + w_L \cdot \text{norm}(l) + w_B \cdot \text{norm}(b)) \times (1 + w_S \cdot \text{norm}(s)) $$
    • norm(): Normalization function to bring metrics l, b, s to a comparable scale (e.g., 0 to 1).
    • w_L, w_B, w_S: Weighting factors determining the relative impact of Lippmann influence, UDQA quality, and Supply/Scarcity. Note that s represents scarcity here (inverse of saturation), so higher s increases the price.
    • This structure allows weighting the source’s influence (l), the response quote’s quality (b), and the quote pair’s rarity/scarcity (s).
  • Requires Definition: Precise metrics for l, b, s; normalization methods; weighting factors w_L, w_B, w_S. Defining b objectively is particularly challenging.

Key Challenge: The primary challenge lies in developing objective, quantifiable metrics for l, b, and s, and agreeing on the appropriate weighting factors (w_L, w_B, w_S) to align with Youseddit’s strategic goals.

Example: Price Decay Over Time

Let’s illustrate the time decay function F_time(t) with hypothetical values:

• Base Price = $3
• Initial Time Multiplier (mult_init) = 5.0 (Quote pair is 5x base price when new)
• Minimum Time Multiplier (mult_min) = 0.5 (Quote pair price never drops below 50% of base * other factors)
• Decay Rate (\lambda) = 0.05 (per day)
• Category Multiplier (F_category) = 1.5 (e.g., for a high-impact topic)
• Relevance Multiplier (F_relevance) = 2.0 (e.g., for a quote pair with a high-influence source, high-quality response, and high scarcity)

Using the formula: $$ F_{\text{time}}(t) = \max(0.5, 5.0 \times e^{-0.05 t}) $$

And the overall price formula: $$ \text{Price}(t) = 3 \times F_{\text{time}}(t) \times 1.5 \times 2.0 = 9 \times F_{\text{time}}(t) $$

Here’s how the price might decay over the first 90 days:

Note: This is a simplified example. The actual decay rate, multipliers, and the point at which the minimum is reached would be determined by platform policy and market analysis. This table provides data points that could be used to generate a visual chart of the price decay curve.

Steelman Justification for the Formula

The strongest argument for adopting this multi-faceted formula rests on its potential to create a nuanced and balanced marketplace for verified information, moving beyond simplistic pricing models.

• Reflects Complex Value, Simply: It acknowledges that a quote pair’s value isn’t monolithic. The formula aims to capture this complexity automatically, providing a fair valuation for the linked initiator and response.
• Balances Market Reality with Quality: By incorporating both source influence (l) and response quality (b), the formula attempts to balance the market’s tendency to overvalue fame with a mechanism to reward substantive, insightful response quotes, regardless of the source’s prominence.
• Mitigates Information Saturation: The scarcity factor (s) addresses the diminishing marginal value of exchanges involving over-exposed figures or topics, preventing inflated prices for readily available information and potentially incentivizing the discovery of less common quote pairs.
• Adaptability through Weighting: The weighting factors (w_L, w_B, w_S) provide crucial flexibility. YouSeddit can strategically adjust these weights over time to fine-tune the market dynamics, perhaps initially emphasizing influence (w_L) but gradually increasing the weight of quality (w_B) or scarcity (w_S) as the platform matures and seeks to differentiate itself.
• Encourages Desired Outcomes: Depending on the weighting, the formula can be tuned to actively encourage specific journalistic behaviors, such as seeking out diverse sources (high w_S), rewarding high-quality responses (high w_B), or prioritizing timely exchanges (high initial F_time).

While acknowledging the significant challenge in objectively measuring b (response quality), this formula provides the most comprehensive framework discussed for capturing the multifaceted nature of quote pair value.

Projected Long-Term Influence

Implementing a pricing model this complex could have significant long-term effects as YouSeddit gains traction:

• Shaping Journalistic Incentives: If the weighting favors quality (w_B) and scarcity (w_S), journalists might be incentivized to seek out less prominent but more insightful sources, potentially diversifying media narratives. Conversely, a heavy emphasis on influence (w_L) could reinforce the focus on established figures.
• Influencing Public Discourse: By potentially making diverse or high-quality quote pairs more accessible or valuable, the model could subtly influence which sources and responses gain prominence in news produced using YouSeddit content.
• Creating a Market for “Quality”: If the b metric (response quote quality) can be implemented effectively, YouSeddit could pioneer a market that explicitly values the substance of the response within its verified context.
• Potential for Gaming/Manipulation: As the model becomes understood, actors might attempt to manipulate the metrics (e.g., artificially boosting perceived influence l, optimizing quotes for perceived quality b). Constant vigilance and refinement of the metrics would be necessary.
• Setting Industry Standards: A successful implementation could influence how other platforms or news organizations value and source information, potentially leading to broader adoption of more nuanced valuation methods.
• Complexity Barrier: The model’s complexity could also be a barrier to adoption or understanding for some users, requiring clear communication and potentially simplified interfaces.

Ultimately, the long-term influence will depend heavily on the specific implementation details, particularly the definition and weighting of the relevance components (l, b, s), and the platform’s ability to measure them accurately and resist manipulation.

User Control

How much control users (journalists, buyers) should have over these pricing factors within the smart contract system needs further discussion.

5 - Relevance Metric: Lippmann Influence (l)

Relevance Metric: Lippmann Influence (l)

This metric attempts to quantify the speaker’s potential influence on public perception, drawing inspiration from Walter Lippmann’s concept of the “pseudo-environment” described in Public Opinion. It focuses on the speaker’s prominence and reach within a specific category or topic.

Conceptual Definition

The l metric represents the degree to which a quote from this individual is likely to shape or reinforce the public’s understanding (the “pseudo-environment”) related to the topic. It aligns with communication theories of opinion leadership, where certain individuals disproportionately influence others’ attitudes or behaviors. Higher influence suggests the quote carries more weight in public discourse simply due to the speaker’s established position or network centrality.

Potential Measurement Factors (Requires Definition)

Quantifying l could involve a composite score based on factors like:

• Media Mentions: Frequency and prominence of the speaker’s coverage in relevant media outlets over a defined period.
• Social Media Reach & Centrality: Follower counts, engagement rates, and discussion volume related to the speaker on major platforms, potentially analyzed using social network analysis metrics (e.g., degree, eigenvector centrality) to gauge influence within online conversations.
• Institutional Role: Formal position or title held by the speaker within relevant organizations (e.g., CEO, Head of State, leading academic).
• Citation Frequency: How often the speaker is cited or referenced by others in the field or media.
• Search Engine Prominence: Volume and ranking of search results related to the speaker and the topic.

Role in Pricing Formula

In the Synthesized Pricing Formula, the normalized l value is weighted (w_L) to contribute positively to the overall price. A higher l generally increases the price, reflecting the perceived market value associated with quotes from highly influential figures.

Challenges

• Objectivity: Defining objective measures and avoiding bias in assessing influence.
• Dynamic Nature: Influence changes over time and requires periodic recalculation.
• Context Sensitivity: Influence can be highly context-dependent (e.g., influential in one field but not another).

Next Step: Develop specific, measurable proxies for influence and define the calculation methodology for the l score.

6 - Relevance Metric: Universal Dialogue Quality Assessment (UDQA)

Relevance Metric: Universal Dialogue Quality Assessment (UDQA)

This metric attempts to capture the intrinsic quality of a quote by assessing fundamental aspects of dialogue quality, applicable to any conversational text snippet, including quotes sourced for Youseddit. It replaces the previous Buber-inspired concept with a more structured, potentially automatable rubric.

Conceptual Definition

The UDQA metric provides a weighted score based on five key dimensions of dialogue quality present within the quote (or the immediate context it’s drawn from). A higher score indicates a quote that is more responsive, clear, helpful, accurate, and safe.

UDQA Formula

For any dialogue snippet $D$ (representing the quote and its immediate context), the quality can be computed as:

$$ \text{UDQA}(D) = \alpha R(D) + \beta C(D) + \gamma H(D) + \delta A(D) + \epsilon S(D) $$

Where:

• $\alpha, \beta, \gamma, \delta, \epsilon$ are weights that sum to 1 (e.g., default weights: 0.25, 0.25, 0.2, 0.15, 0.15).
• $R(D), C(D), H(D), A(D), S(D)$ are the scores for each component metric (normalized 0-1).

Component Metrics

1. Responsiveness $R(D)$:
   • Focus: Does the quote directly address the points or questions raised in the preceding context (e.g., the interviewer’s question)?
   • Formula: $$ R(D) = \frac{\text{Number of initiator points addressed in response}}{\text{Number of addressable points in initial query}} $$
2. Clarity $C(D)$:
   • Focus: Is the quote unambiguous and easy to understand?
   • Formula: $$ C(D) = 1 - \frac{\text{Ambiguous or vague statements}}{\text{Total statements in response}} $$
3. Helpfulness $H(D)$:
   • Focus: Does the quote provide information or perspective that progresses the goal of the conversation or adds value?
   • Formula: $$ H(D) = \frac{\text{Information or actions that progress conversation goal}}{\text{Total information or actions in response}} $$
4. Accuracy $A(D)$:
   • Focus: Does the quote contain identifiable factual errors or inconsistencies? (Requires external knowledge or comparison).
   • Formula: $$ A(D) = 1 - \frac{\text{Identifiable factual errors or inconsistencies}}{\text{Total factual claims made}} $$
5. Safety $S(D)$:
   • Focus: Does the quote avoid potentially harmful, biased, or inappropriate content?
   • Formula: $$ S(D) = 1 - \frac{\text{Potentially harmful, biased or inappropriate content}}{\text{Total response content}} $$

Practical Application & Role in Pricing

An automated system (potentially an LLM monitor) could analyze quotes against these dimensions.

In the Synthesized Pricing Formula, the normalized UDQA score (let’s still call the variable b for consistency with the formula) is weighted (w_B) to contribute positively to the overall price. A higher b (UDQA score) increases the price, reflecting a premium placed on quotes deemed to possess higher intrinsic quality based on this rubric.

Challenges

• Quantification: Defining precise, objective methods for counting “addressable points,” “ambiguous statements,” “helpful actions,” “factual claims,” and “harmful content” within a quote is challenging, especially for automated systems.
• Context Dependency: Assessing responsiveness and helpfulness requires understanding the surrounding dialogue context. Accuracy requires external fact-checking.
• Weighting: Determining the appropriate weights ($\alpha, \beta, \gamma, \delta, \epsilon$) is subjective and depends on platform priorities.
• Scalability: While potentially more automatable than purely philosophical assessments, robust NLP analysis for all dimensions can be computationally intensive.

Next Step: Develop and evaluate NLP models or clear guidelines for assessing each UDQA component score for quotes within the YouSeddit system.

7 - Relevance Metric: Supply/Scarcity (s)

Relevance Metric: Supply/Scarcity (s)

This metric applies fundamental microeconomic principles of supply and demand to the market for quotes. It acts as a counterweight or modifier to pure speaker influence (l), acknowledging that high availability (supply) can diminish the marginal value and equilibrium price, especially considering the unique characteristics of information goods where reproduction costs can be low (though verification adds cost).

Conceptual Definition

The s metric represents the scarcity of verified quotes from a particular source or about a specific niche topic within the YouSeddit platform and potentially the broader public domain. A higher s value indicates greater scarcity (lower supply/saturation), suggesting the quote is rarer and potentially more valuable from a supply-side perspective.

Potential Measurement Factors (Requires Definition)

Quantifying s could involve factors like:

• Volume of Existing Quotes (Speaker): The total number of verified quotes already available from the specific speaker within the YouSeddit system and potentially estimated from external sources. Higher volume leads to lower s.
• Volume of Existing Quotes (Topic): The total number of verified quotes available on the specific, narrow topic. High volume leads to lower s.
• Frequency of Speaker’s Public Statements: How often the speaker makes public statements or gives interviews. Frequent statements suggest lower scarcity (lower s).
• Exclusivity / Differentiation: Is the quote exclusive to the YouSeddit platform or widely disseminated? Higher exclusivity, a form of product differentiation or artificial scarcity, increases the s value.
• Time Since Last Quote: A longer time since the speaker was last quoted on the topic might increase perceived scarcity (s).

Role in Pricing Formula

In the Synthesized Pricing Formula, the normalized s value (representing scarcity) is weighted (w_S) to contribute positively to the overall price. A higher s (meaning greater scarcity/lower saturation) increases the price, reflecting the premium associated with rarer information. This directly incorporates the “Inverse Relevance Pricing” perspective by ensuring that readily available quotes (low s) are not overpriced solely due to speaker influence (l).

Challenges

• Defining Scope: Determining whether to measure scarcity only within YouSeddit or attempt to estimate broader public availability.
• Data Acquisition: Gathering reliable data on external quote availability is difficult.
• Topic Granularity: Defining topic boundaries for scarcity measurement can be ambiguous.

Next Step: Define the precise scope (internal vs. external) and develop methods for tracking quote volume per speaker and topic to calculate the s score.

8 - Simulation: Freelance Videographer (State Legislature)

Simulation: Freelance Videographer (State Legislature)

This simulation models potential income for a freelance videographer specializing in state legislative sessions, using YouSeddit to license short, verified video clips containing key quotes or moments from debates, hearings, or interviews. This scenario assumes income follows a typical legislative calendar, with peaks during the main session and lulls during recesses.

Disclaimer: This simulation uses highly simplified assumptions about clip value, licensing frequency, and seasonality. Actual income depends on the specific events covered, the impact of the clips, market demand, and finalized model parameters.

Simulation Assumptions

• Asset: Short, verified video clips (containing quotes/key moments) licensed via Youseddit.
• Base Initial Clip Price (Avg): $25.00 per license (Assumed higher value for video & political relevance; BasePrice=$5, F_time=5.0, F_cat=1.0, F_rel=1.0).
• Journalist’s Share: 90%.
• Base Initial Revenue per License: $25.00 * 90% = $22.50.
• Price Decay Function: F_time(t) = max(0.5, 5.0 * e^(-0.05 * t)) (Same decay as text quotes for simplicity).
• Base Licensing Pattern (Avg per Clip):
  • Month 1 (Avg Age ~15 days): 4 licenses purchased (Higher initial interest assumed).
  • Month 2 (Avg Age ~45 days): 2 licenses purchased.
  • Month 3 onwards: 0 licenses purchased (Total 6 licenses per clip).
• Base Revenue Calculation:
  • Month 1 Revenue/License ≈ $22.50 * (F_time(15) / F_time(0)) ≈ $22.50 * (2.36 / 5.0) ≈ $10.62
  • Month 2 Revenue/License ≈ $22.50 * (F_time(45) / F_time(0)) ≈ $22.50 * (0.53 / 5.0) ≈ $2.39
• Seasonality / Event Adjustments (State Legislature Cycle):
  • Session Peak (Jan-May): 150% production (30 clips/month) AND 150% licenses purchased (6 for new, 3 for previous).
  • Session Wind-Down (June): 100% production (20 clips/month) AND 100% licenses purchased (4 for new, 2 for previous).
  • Off-Session/Summer (Jul-Aug): 25% production (5 clips/month) AND 25% licenses purchased (1 for new, 0 for previous - simplified).
  • Interim/Pre-Session (Sep-Dec): 75% production (15 clips/month) AND 75% licenses purchased (3 for new, 1 for previous - simplified).
• Assumed Direct Project Income (Monthly): $5,000 (Jan-May), $3,500 (Jun), $1,000 (Jul-Aug), $2,500 (Sep-Dec). Illustrative, varies greatly.

Monthly Income Simulation (Freelance Video Journalist)

Conclusion

This simulation, incorporating seasonality for both direct project work and YouSeddit licensing based on a state legislative calendar, shows:

• Amplified Seasonality: Total monthly income shows extreme peaks during the legislative session (Jan-May, ~$7100/month) and deep troughs during the off-session (Jul-Aug, ~$1050/month).
• Significant Supplement during Peak: The YouSeddit supplement provides a substantial boost (~$2100/month) during the busiest legislative period.
• Modest Off-Season Contribution: YouSeddit income is minimal during the off-season but still provides some revenue when direct project work is scarce.
• Total Annual Potential: The total estimated supplementary income from YouSeddit over 12 months remains approximately $13,500, added on top of the assumed direct project income (~$40,500).

For a freelance videographer focused on state legislatures, YouSeddit offers a way to further capitalize on peak session activity and provides a small income floor during off-peak times. The overall income pattern remains highly seasonal.

{{/* Removed footnote [^1] as base salary assumption was removed */}}