Being, Noticing, Knowing: The Emergence of Resilience in Group Work: Jeremy Woodcock
Bibliographic record
Abstract
Fifteen years ago, few people had heard of HIV; now HIV/AIDS is one of the most serious health and social issues facing societies worldwide (Frank, 1996 ). Women represent one of the fastest growing groups for HIV infection. Taylor-Brown and Wiener ( 1993 ) report that the majority of HIV-positive women in the United States are of childbearing age and have dependent children. In a conservative estimate, they suggest that 80,000 children in the United States will be orphaned by the year 2000 as a result of AIDS. Similar statistics are not available for Canada. Recently, great strides have been made with the introduction (in developed countries) of new combination antiretroviral drugs, and, as a result, people infected with HIV now have longer life expectancies than ever before (Reiter, 1998 ). Further, fewer children are being infected through vertical transmission (from mother to child) (Kotler, 1998 ), and we may well anticipate that fewer minors will become orphans. In spite of these important medical discoveries, however, the social impact of living with HIV/AIDS continues to be tremendous. Secrecy, stigma, and fears of discrimination continue to be central features of living with HIV/AIDS and affect if, when, and how families will share with their children the HIV-positive status of a loved one (Niebuhr, Hughes, and Pollard, 1994 ; Salter Goldie et al., 1997 ; Wiener, Riekert, and Pizzo, 1997).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".