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Record W3014599762 · doi:10.1177/1077695820912953

Pushing Boundaries: How Coding Is (and Isn’t) Taught in Accredited Journalism Programs

2020· article· en· W3014599762 on OpenAlexaboutno aff
James C. Foust, Katherine A. Bradshaw

Bibliographic record

VenueJournalism & Mass Communication Educator · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsJournalismAccreditationCoding (social sciences)CensusQuarter (Canadian coin)Public relationsPolitical scienceSociologyMedia studiesGeographySocial scienceLaw

Abstract

fetched live from OpenAlex

A census of Accrediting Council on Education in Journalism and Mass Communications (ACEJMC)–accredited journalism programs reveals that less than a quarter require students to learn code. Despite industry desires for journalists with coding skills, nearly 40% of the units offer no coding classes. Among programs that require code, most rely on a course or courses taught by full-time faculty in the accredited unit. About one third of units that do not require code currently have plans to add it in the future.

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)0.126
metaresearch head score (Gemma)0.379
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 score0.126
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1260.379
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0180.022
Scholarly communication0.0190.021
Open science0.0070.023
Research integrity0.0050.011
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 head0.061
GPT teacher head0.333
Teacher spread0.272 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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