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Record W2888840845 · doi:10.3390/sports6030087

Analysis of the Coverage of Paratriathlon and Paratriathletes in Canadian Newspapers

2018· article· en· W2888840845 on OpenAlexaffabout
Gregor Wolbring, Brian Martin

Bibliographic record

VenueSports · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNewspaperEliteRecreationContent analysisThematic analysisAthletesTheme (computing)Public opinionPolitical scienceAdvertisingPublic relationsMedia studiesPsychologySociologyQualitative researchSocial scienceLawPoliticsBusiness

Abstract

fetched live from OpenAlex

From recreational to elite levels, sport has many benefits for disabled people. At the same time, it is acknowledged that there is a trickle-down problem from para-elite sport to sport participation of disabled people, in general. Newspapers are one form of media that sets agendas and influences public opinion. Many studies have highlighted problematic aspects of parasport and para-athlete coverage in newspapers. Paratriathlon was one of two new events added to the Paralympics in Rio 2016, which increased its visibility in the public domain. We investigated the coverage of paratriathlon and paratriathletes in 300 Canadian newspapers using the ProQuest database Canadian Newsstream as a source, and utilizing a descriptive quantitative and a qualitative thematic content analysis. The main themes evident in the reporting on paratriathlon and paratriathletes, in the three hundred Canadian newspapers we covered, were the supercrip imagery of the para-athlete, personal stories mostly linked to the supercrip imagery, and the theme of able-bodied athletes in juxtaposition to the para-athletes. Using the lens of the four legacy goals of the International Paralympic Committee, we conclude that our findings are detrimental to the fulfillment of the four legacy goals.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.312
Teacher spread0.302 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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