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Record W7024381368

Sexuality, Sport, and the City: Sporting Mega-Events and the Spatial Politics of Canadian Sexual Citizenship

2023· dissertation· en· W7024381368 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipPoliticsGovernment (linguistics)Field (mathematics)Human sexuality
DOInot available

Abstract

fetched live from OpenAlex

Introduction From these thirty issues I pulled out of each of these daily periodicals, I ended up with 59 articles from Le Devoir, 67 from The Toronto Star, 63 from The Globe and Mail, and 57 from The Vancouver Sun.As the Body Politic was published monthly or bi-monthly, I did not cap the number of issues.Out of the 24 Body Politic issues published in 1975-1977, 79 articles contained keywords and were included in the scope.I could not find a database of print articles from Xtra Magazine, and so I looked at the digital editions of the magazine only.Xtra Magazine published in print bi-weekly, but published two articles each week in the digital magazine.I selected 56 articles which were the most relevant, meaning they included at least one keyword.I stopped when I reached saturation: the same articles were repeatedly shown up in my search, and no new articles contained keywords.3.4 Coding and analysis After selecting the articles within my scope for each periodical, I read each individual article.My coding process involved assigning different colours to my original keywords, and while going through each article, highlighting phrases which related to each keyword in its assigned colour.I also noted other keywords and concepts which were present across different articles.These keywords and concepts became my 'codes'.In this way, I developed a corpus of codes, constructed from the original keywords I used to select my sources, as well as new keywords and concepts that were present in multiple articles.Mayring (2002, p. 120) calls this approach to textual analysis 'evolutionary coding'.My keywords then developed from theoretical considerations into an operational list based on the newspaper data.While colour-coding information in the articles, I created a list of the statements made on each particular code, and a brief description of the tone of the article, word choice of the author and any political leanings.I

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)0.000
metaresearch head score (Gemma)0.001
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 score0.038
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.007
Scholarly communication0.0070.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.000

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 head0.039
GPT teacher head0.291
Teacher spread0.252 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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