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Record W4403532592 · doi:10.1080/21670811.2024.2396551

Why Infrastructure Studies for Journalism?

2024· article· en· W4403532592 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDigital Journalism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council
KeywordsJournalismComputer sciencePublic relationsPolitical scienceMedia studiesSociology

Abstract

fetched live from OpenAlex

This article makes a case for the value of infrastructure studies in analyzing journalism’s evolving landscape. It argues that infrastructural thinking is valuable to understand the changing neighbourhood of journalism, encompassing not just newsrooms but also the broader sociotechnical systems and resources, values and practices shaping news production, distribution, and consumption. Through a careful reading of the literature on infrastructure thinking and its application to journalism studies, it highlights how infrastructural thinking has been used for new and conventional research objects from micro, meso and macro levels of the field. We identify four themes that journalism scholars have focused on: journalism institutions, platforms and platform power, adjacent institutions and applications, and sociotechnical apparatus. We argue that infrastructure studies provides a timely two-way lens to deconstruct the “always there” nature of prior journalism systems and how they shape both the scope of scholarly inquiry and journalism.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.045
GPT teacher head0.366
Teacher spread0.321 · 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