Sexuality, Sport, and the City: Sporting Mega-Events and the Spatial Politics of Canadian Sexual Citizenship
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".