Spaces of Negotiation: Analyzing Platform Power in the News Industry
Bibliographic record
Abstract
This article develops an analytical framework to examine the contingent power relations between news organizations and platforms. Eschewing one-sided, monolithic perspectives on platform dominance, we instead theorize power as relational. From this perspective, we observe important variations in news organizations’ degree of platform in/dependence. Examining these variations, we propose the concept of spaces of negotiation, which refers to the opportunities available to news organizations to determine how they produce, distribute, and monetize content vis-à-vis platforms. Building on research in journalism studies, platform studies, and related disciplines, we identify three key variables that shape these spaces of negotiation: (1) platform evolution, (2) stage of production, and (3) type of news organization. A systematic analysis of these variables, we contend, allows for a more nuanced, less deterministic understanding of the role of platform companies in transforming the news landscape.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".