Bibliographic record
Abstract
During Covid-19, health care workers have been vulnerable to death, and at the same time, in response to their vulnerability, heroic. Heroism is one of the most ubiquitous narratives during this pandemic. In this article, I am interested in the juncture between vulnerability and heroism, the discursive privileging of a hero and the implications of this for social workers in health and social care. I use the writings of Judith Butler to ask, where has vulnerability gone? I argue that it is not that vulnerability is erased or suppressed, or comes second in the public imaginary, but rather, vulnerability is reconstituted as heroic and becomes unrecognisable. Vulnerability is an under-examined concept in social work and an analysis of its cultural representation during the outbreak of Covid-19, can contribute to our knowledge about how vulnerability operates in health and social care, as well as how vulnerability conditions the cultural spaces we operate within. Can new insights, provoked by the cultural responses to this pandemic, lead to a reorientation for social work politics and the politics of vulnerability?
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| 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".