LOCATING AND THEORIZING PLATFORM POWER
Bibliographic record
Abstract
This panel locates and theorizes platform power through five case studies, focussing on: 1) video sharing platforms, 2) app stores, 3) programmatic advertising networks, 4) labor staffing intermediaries, and 5) cloud computing. Each case study starts with the question: where do relations of dependence take shape on the examined platform(s) and how are these relations organized? Addressing this question, the panelists hypothesize that platform power is exerted, codified, and operationalized around particular infrastructural platform services, which enable specific economic activities, such as advertising, content sharing, data analysis, labor staffing and management, cloud hosting, and so on. Examining these services, the panelists specifically focus on the evolution of platforms. Infrastructural services, such as Facebook Reels or the Apple’s App Store each set standards and provide gateways for complementors–content and service providers, advertisers, data intermediaries, talent agencies–to access other institutional actors, data, and end-users. Yet, such services are also constantly adapted to local regulatory frameworks, to retain end-users and complementors, and to respond to competitors in platform ecosystems. In turn, such changes force complementors to adapt their own operations to continue offering their products and services through the platform. It is in these moments of change, when relations of dependence are reshuffled, that platform power becomes most visible. In combination, the five case studies will provide more detailed insights into how and where relations of dependence take shape in the platform ecosystem and how these relations evolve over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".