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Record W2890393924 · doi:10.1123/ijsc.2018-0088

Social Media and Digital Breakage on the Sports Beat

2018· article· en· W2890393924 on OpenAlexaff
Mark Douglas Lowes, Christopher Robillard

Bibliographic record

VenueInternational Journal of Sport Communication · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsJournalismGatekeepingPublic relationsCitizen journalismSocial mediaSociologyMedia studiesConnotationObjectivity (philosophy)Political scienceAdvertisingLawBusiness

Abstract

fetched live from OpenAlex

This scholarly commentary draws on existing sport communication literature in an exploration of social media’s role in, and impact on, sport journalism practices and the production of sport news. Of particular concern is the emergence of a form of citizen sport journalism that usurps the traditional role of sport journalists as gatekeepers of the relationship between the sports world and its multitude of audiences. It is argued that social media are providing audiences with more opportunities to create the type of mediated discourses they want to experience by eliminating the scarcity of time and space that once privileged the gatekeeping status of sport journalists. Consequently, sport reporters are becoming social-media content creators and curators while competing against spectator sport-news content creators. Whereas these changes might have a negative connotation, the authors conclude that sport coverage in digital culture offers more opportunities for journalists to step outside the confines of traditional sport journalism work routines and news-production practices.

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)0.001
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.006
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.002

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 head0.329
Teacher spread0.292 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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