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Record W2731803017 · doi:10.1177/0163443717714992

Changing work routines and labour practices of sports journalists in the digital era: a case study of Postmedia

2017· article· en· W2731803017 on OpenAlexaffabout
Evan Daum, Jay Scherer

Bibliographic record

VenueMedia Culture & Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsJournalismAmateurLeagueCitizen journalismPublic relationsDigital mediaNegotiationPolitical scienceSociologyFootballDemocracyMedia studiesAdvertisingSocial scienceLawBusinessPolitics

Abstract

fetched live from OpenAlex

This article contributes to an emerging body of research that examines the transformation of sport, journalism and media practice in the digital era as part of what Raymond Williams has called the ‘long revolution’ of communications, culture and democracy. In so doing, we explore how Canadian sports journalists have attempted to make sense of, and negotiate their roles within, the practice of convergent sports journalism and the ascension of new online journalism values in the Postmedia Network. We examine the institutionalization of 24/7 digital sports departments within which Postmedia’s sports journalists labour to produce a continuous flow of coverage of major league sport – at the expense of local amateur events and women’s sport – to secure a digital audience commodity of male readers. We also explore Postmedia’s embracement of outsourced labour and production processes that have further altered the work routines of sports journalists and have undermined quality standards. Finally, we underscore how the expansion of the digital promotional networks of major league sport has contributed to the ongoing historical erosion of the status and influence of sports journalists in the sports–media complex and has spurred the rise of derivative analytical and opinion-driven content.

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)0.005
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0210.010
Scholarly communication0.0080.004
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.038
GPT teacher head0.338
Teacher spread0.301 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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