Bibliographic record
Abstract
Abstract The resurgence of grass-roots activism around race (#BlackLivesMatter) and class (anti-austerity, Occupy) has highlighted how matters of age collide with other significant determinants of health and illness, such as gender, sexuality, race, ethnicity, and class. Clearly, what it means to grow old is deeply contingent upon far-reaching disciplinary and contextual factors that shape students’ understanding of health well before they step into the classroom. Part one of this chapter unpacks key problems currently facing age studies pedagogy by asking, How can age studies be taught to better reflect these crucial diversities, encourage the growth of this field, and establish the value of age studies for students from a range of academic backgrounds and with assorted, often uncertain, career paths? Part two expands this proposition through elaborating a case study of teaching age studies as part of the health humanities curriculum at the University of Toronto Scarborough. The author outlines ways in which striking but conventional age studies material (e.g., Shakespeare’s King Lear) might be repurposed to respond meaningfully both to the locality of a multicultural teaching environment and to students who may possess very different frames of reference. The purpose of the chapter is to (1) articulate the need for greater diversity in age studies (lessons reflected in health humanities more generally) and (2) provide concrete ways to embolden such diversification within the classroom by engaging students—and charging educators—in the expansion of what humanistic studies of aging might entail.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".