Research Methods
Extraction Patterns
Scoring Framework
A methodology for analyzing news headlines to quantify how media discourse obscures labor, erases expertise, and misrepresents the value flow of institutional capacity.
The Problem We're Measuring
Institutions extract value—labor, expertise, narratives, cultural knowledge—from communities and individuals, then present that value as if it originated within the institution itself. The result: communities lose recognition, economic benefit, and historical narrative authority.
What We Measure
- ✦ Visibility of institutional labor
- ✦ Attribution of expertise and knowledge
- ✦ Transparency of value flows
- ✦ Acknowledgment of systemic lineage
What This Reveals
- ✦ Patterns of institutional erasure
- ✦ Media accountability in naming origins
- ✦ Where value actually flows
- ✦ Systemic vs. individual narratives
The Four Axes
Does the headline acknowledge the human capacity, systems, and labor that made this possible?
Scoring Rubric (0.0 → 1.0)
Institution appears fully autonomous; no mention of labor, infrastructure, or capacity
Clearly attributes institutional capacity to the people, systems, and labor that built it
Brief mention of "staff" or "team" but no specificity about what systems enable the work
Acknowledges care/infrastructure exists but attributes it vaguely or to the institution
Names specific infrastructure or roles but doesn't center their labor or complexity
Is the expertise, knowledge, and lived experience that created this solution properly attributed and visible?
Scoring Rubric (0.0 → 1.0)
Expertise is erased; solution appears to emerge from nowhere or from the institution alone
Centers the originators' expertise, names them clearly, and credits the intellectual lineage
Expertise is mentioned but attributed to the institution, not the originators
Names some originators but frames their contribution as "input" rather than authorship
Clearly identifies originators and their expertise but downplays the intellectual labor
Does the story name where value flows? Who profits, who subsidizes, who bears the cost?
Scoring Rubric (0.0 → 1.0)
No mention of money, profit, cost, or economic benefit to anyone
Makes visible the full value chain: who created value, who captured it, who subsidized it
Mentions profit/benefit to institution but not who paid for it or whose labor was unpaid
Names some economic benefit but obscures who bears the cost of provision
Discusses economic benefit clearly but minimizes or hides extraction/subsidy
Does the headline acknowledge systemic lineage? Is this a new solution or a repetition of historical patterns?
Scoring Rubric (0.0 → 1.0)
Treats solution as entirely new; no mention of precedent, repetition, or systemic history
Explicitly names how this reproduces historical extraction patterns and systemic lineage
Brief reference to "history" or "past attempts" but no analysis of patterns
Names historical precedent but frames current work as distinct or improved
Acknowledges systemic repetition but frames it as inevitable or technical
Reading Your Scores
High Score (0.7+)
The headline is transparent about who did the work, where value flows, and how this fits into systemic history. It names origins and attributes expertise.
Medium Score (0.4–0.7)
The headline acknowledges some infrastructure or attribution but obscures key elements. Partially transparent but incomplete credit.
Low Score (0.0–0.4)
The headline erases origins, hides value flows, or treats solutions as autonomous institutional achievements. Significant extraction patterns.
Limitations & Caveats
Headlines are constrained by length. A fully transparent story may still produce a low headline score due to brevity.
This framework measures extraction patterns in media framing, not absolute truth. A low-scoring headline may accompany a rigorous article.
Some extraction is structural and invisible to headlines: unpaid background labor, community knowledge transfer, etc.
The framework is designed for English-language news and may require adaptation for other languages and media types.
Automation via LLM has biases. Regular human review is essential for QC and framework refinement.
Real-Time Emotion Metrics
Our daily emotion metrics aggregate headline analysis scores to provide a real-time snapshot of media emotional direction. These metrics are calculated from all headlines analyzed that day across the system.
Measures how much the headline acknowledges human labor, care work, and institutional capacity behind outcomes.
Calculation: Average of all CI scores from headlines analyzed today. Scored 0.0–1.0 based on capacity mention + attribution clarity.
Measures visibility of expertise, authorship, and the origin of knowledge or solutions.
Calculation: Average of all RG scores from headlines analyzed today. Scored 0.0–1.0 based on expertise attribution + origin clarity.
Measures how clearly the headline shows where economic value flows and who benefits.
Calculation: Average of all ER scores from headlines analyzed today. Scored 0.0–1.0 based on profit visibility + community benefit acknowledgment.
Measures whether the headline places events in systemic, historical, or lineage context.
Calculation: Average of all HC scores from headlines analyzed today. Scored 0.0–1.0 based on systemic pattern recognition + historical lineage.
Emotional Direction: Liberation vs Control
Beyond the four axes, each headline is classified by its emotional vector—whether it moves toward human liberation or institutional control.
Liberation
Headlines that emphasize human agency, community capacity, systemic accountability, or collective power. Language of freedom, repair, and possibility.
Control
Headlines that emphasize institutional authority, compliance, surveillance, punishment, or constraint. Language of management, enforcement, and limitation.
Calculation: Percentage of today's headlines classified as Liberation vs Control. Neutral headlines are excluded from the percentage but counted in total.
Update Frequency: Metrics refresh automatically as new headlines are analyzed. Scores represent a rolling daily average.
Data Source: All headlines in the HeadlineAnalysis entity with analysis_date matching the current date (America/Indianapolis timezone).
Cite This Methodology
McAleavey, M. (2026). "Extraction Patterns Scoring Framework: A methodology for analyzing media coverage of institutional value flows." Journal of Economic Iatrogenesis & Labor Studies. Available at joy-repair.org/methodology.
Version 1.0 — Last updated April 2026. This methodology is open for peer review and refinement. Comments welcome at [framework feedback].