Shit, Stress, and Sacrifice: The Lived Experiences of Women's Cross-Generational Caregiving
Bibliographic record
Abstract
The following script was written be performed as presentation. Reader's Theatre is a staged presentation of piece of text or selected pieces of different texts that are thematically linked (Donmoyer & Yennie-Donmoyer 1995, 406). It is an oral interpretation in which scripts are held and read rather than acted and vocal expression is used to give the listener vivid picture of the action (Dixon et al. 1996, 13). You as the audience are invited create meaning from what is conveyed in the text. The objective of this Reader's Theatre is use our personal narratives bring life and critique the academic literature relevant women's caregiving. The intention is not provide answers, but stimulate lively discussion about caregiving in society that has thus far refused recognize and value essential emotional labour; labour that is provided mostly by women. Reader's Theatre, non-traditional format (Brodie & Wiebe 1999; Ellis & Bochner 1996), was chosen invite inspiring and meaningful dialogue. We hope by the end of the presentation the audience agrees that many women's lives are worthy of recognition for what we accomplish in day, what we fail accomplish, and the price we pay for the attempt.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".