Bibliographic record
Abstract
Ashraf Zanati (see Figure 7.1), a former teacher and one of 52 men arrested in Cairo, Egypt, for ‘debauchery’ in 2001, in an incident which would stimulate worldwide attention concerning gay identity in the developing world, tells us (in the documentary Dangerous Living: Coming Out in the Developing World (John Scagliotti, 2001, US)): I stayed in prison for 13 months. I tried to make myself quite useful, I adapted myself. I thought that I am there for a reason so I started to teach people in prison, English. I taught about 50 people in prison…. Now I am leaving [my home] behind. I am leaving everything behind me, even my memories. My mum is very attached to me, and when I told her that I am leaving, she couldn’t believe it, and she said to me ‘try again to be here’. But I couldn’t. Zanati’s testament reveals the vulnerable nature of sexual nonconformity within the developing world. 1 Not only was he arrested for simply attending a social event, and inordinately punished as part of a government campaign to limit gay visibility within Egypt, but also the context of imagined democracy within the Western (developed) world plays a significant role in his identity expectations. Unable to resolve the oppressive situation within his own country, in order to find a more fulfilled sense of self (as a gay man) he must leave for the West, eventually becoming a refugee in Canada. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".