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Record W3092149140 · doi:10.1386/jdmp_00025_1

Vampire squids, ‘the broken internet’ and platform regulation

2020· article· en· W3092149140 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

VenueJournal of Digital Media & Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsCarleton University
Fundersnot available
KeywordsVampireThe InternetJournalismCriticismFunction (biology)DemocracyAdvertisingBusiness modelBusinessPolitical scienceLaw and economicsMedia studiesEconomicsSociologyLawWorld Wide WebComputer scienceMarketing

Abstract

fetched live from OpenAlex

Google, Apple, Facebook, Amazon, Microsoft and Netflix have come under intense criticism for acquiring undue influence on the media, economy, society and democracy. Google and Facebook’s business models, especially, are cast as a form of ‘vampire economics’ responsible for the crisis of journalism and upending the media industries. Many media scholars argue that since the platforms increasingly function like media companies, media policy should be our North Star with respect to what new approaches to internet regulation should look like. This article agrees that a forceful response to the platforms is overdue but criticizes the case against them for too often resting on cherry-picked evidence and an exaggerated sense of their clout, while references to media policy obscure a better approach that draws on four principles from telecoms regulation to guide a new generation of internet regulation: structural separation, line of business restrictions (i.e., firewalls), public obligations and public alternatives.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.321
Teacher spread0.266 · 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