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Record W2465888519 · doi:10.1123/ijsc.2016-0010

“Birds of a Feather”: An Institutional Approach to Canadian National Sport Organizations’ Social-Media Use

2016· article· en· W2465888519 on OpenAlexaffabout
Michael L. Naraine, Milena M. Parent

Bibliographic record

VenueInternational Journal of Sport Communication · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThematic analysisNormativeContent analysisFollowershipSocial mediaSalientPublic relationsExploratory researchSociologyExploratory analysisQualitative researchSocial psychologyPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to examine sport organizations’ social-media activity using an institutional approach, specifically, to investigate the main themes emanating from Canadian national sport organizations’ (CNSOs) social-media communication and the similarities and differences in social-media use between the CNSOs. An exploratory qualitative thematic analysis was conducted on 8 CNSOs’ Twitter accounts ranging from 346 to 23,925 followers, with the number of tweets varying from 219 to 17,186. Thematic analysis indicated that CNSOs generally used tweeting for promoting, reporting, and informing purposes. Despite the organizations’ differing characteristics regarding seasonality of the sport, Twitter-follower count, total number of tweets, and whether the content was original or retweeted, themes were generally consistent across the various organizations. Coercive, mimetic, and normative isomorphic pressures help explain these similarities and offer reasons for a lack of followership growth by the less salient CNSOs. Implications for research and practice are provided.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.056
GPT teacher head0.321
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations29
Published2016
Admission routes2
Has abstractyes

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