Narrative Testimony as Theoretical Method: Examining the Critical Debate on the Culture of Policing Homosexuality in the Jamaican Context
Bibliographic record
Abstract
Author(s): Ronald Cummings | Роналд Камингс Title (English): Narrative Testimony as Theoretical Method: Examining the Critical Debate on the Culture of Policing Homosexuality in the Jamaican Context Title (Macedonian): Наративното сведоштво како теориски метод: Испитување на критичката дебата за културата на надзор врз хомосексуалноста во јамајкански контекст Translated by (English to Macedonian): Kalina Janeva | Калина Јанева Journal Reference: Identities: Journal for Politics, Gender and Culture, Vol. 6, No. 2-3 (Summer 2007 - Winter 2008) Publisher: Research Center in Gender Studies - Skopje and Euro-Balkan Institute Page Range: 177-199 Page Count: 20 Citation (English): Ronald Cummings, “Narrative Testimony as Theoretical Method: Examining the Critical Debate on the Culture of Policing Homosexuality in the Jamaican Context,” Identities: Journal for Politics, Gender and Culture, Vol. 6, No. 2-3 (Summer 2007 - Winter 2008): 177-199. Citation (Macedonian): Роналд Камингс, „Наративното сведоштво како теориски метод: Испитување на критичката дебата за културата на надзор врз хомосексуалноста во јамајкански контекст“, превод од англиски Калина Јанева, Идентитети: списание за политика, род и култура, т. 6, бр. 2-3 (лето 2007 - зима 2008): 177-199.
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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 | 0.037 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".