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Record W4292183939 · doi:10.1080/21670811.2022.2103011

Spaces of Negotiation: Analyzing Platform Power in the News Industry

2022· article· en· W4292183939 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDigital Journalism · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNegotiationDominance (genetics)JournalismPower (physics)Key (lock)Perspective (graphical)Computer scienceKnowledge managementBusinessPolitical scienceAdvertisingComputer securityLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.311
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it