Dressing Up and Dressing Down: Costumes, Risky Play, Transgender, and Maritime English Canadian Charivari Paradoxes
Bibliographic record
Abstract
Why should it seem appropriate to wear a wedding gown at some charivaris, cross dress and be masked at others, but have entirely unremarkable clothing at most? The author's question echoes one from anthropologist Edmund R. Leach. In “Time and False Noses,” he asks “Why should it seem appropriate to wear top hats at funerals, and false noses on birthdays and New Year's Eve?” For Leach, dressing both up and down, despite their symbolic differences–formality versus informality; seriousness versus play--demonstrates a contrast with everyday life. Formality and masquerade alike appear in some practices in Canada related to charivari, a rudimentary form of folk drama. But perhaps equally compelling is the fact that they need not be there, and in most cases do not manifest, as my opening query indicates. The author explores this somewhat paradoxical situation, drawing on interviews and questionnaire responses given by participants in the tradition.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".