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Record W2794453927 · doi:10.21810/strm.v10i1.251

I/O: Reinforcing Newsmaking Practices Through Algorithmic Media

2018· article· en· W2794453927 on OpenAlexaffvenue
Luciano Frizzera

Bibliographic record

VenueStream Interdisciplinary Journal of Communication · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsConcordia University
Fundersnot available
KeywordsGatekeepingObjectivity (philosophy)News valuesComputer scienceDominance (genetics)ModalitiesDigital mediaNews mediaAdvertisingPolitical scienceInternet privacyBusinessSociologyWorld Wide WebEpistemologySocial science

Abstract

fetched live from OpenAlex

Recent developments in communication and information technology have disrupted the long-established dominance of mass media over the production and distribution of news. As an effort to reclaim their role of society’s information gatekeeper, media companies absorb digital technology as instruments of institutional power to reproduce its own logic in the digital space. This paper dis-cusses two interrelated modalities of algorithmic news: economically efficient production, where news outlets utilize quantitative metrics to improve content effectiveness and desirability; and shared-gatekeeping, where visibility and distribution of information are contextual and based on users’ behaviour. The paper proposes that algorithmic media hides under its supposed objectivity and neutrality to become a new gatekeeper “organism”, which not only regulates flows of infor-mation, but also interprets and negotiates both public interests and the value of the news.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0060.006
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.066
GPT teacher head0.419
Teacher spread0.353 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes2
Has abstractyes

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