Margaret Crukshank. Learning to Be Old: Gender, Culture, and Aging. Lanham, MD: Rowman & Littlefield, 2003.
Bibliographic record
Abstract
RÉSUMÉ Dans une perspective interprétative féministe, Cruikshank se penche sur les questions de race, de genre, d'ethnicité, de santé et de spiritualité liées au monde des personnes âgées. L'auteur fournit une description très vivante des questions traitées et présente des points de vue qui sont suffisamment arrêtés pour stimuler le débat, et suffisamment documentés pour être crédibles. Il s'agit d'une approche à la fois critique et créative, qui définit un éventail de construits sociaux ayant un impact positif sur un vieillissement sain et intégré, tout en soulignant la nécessité d'un changement profond du contexte culturel. Bien qu'il défie les normes reçues, ce livre n'en constitue pas moins une ode au vieillissement et fournit des arguments convaincants en faveur de l'optimisme et de la croissance personnelle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".