The Story of the Old Butcher’s Wife and Other Tales. Self-derision and Creating a Sense of Sameness in Care Processes
Bibliographic record
Abstract
This paper takes as its starting part a story told by psychiatrist-psychoanalyst Jean Oury which is then explored within the context of similar tales related by carers and in which self-derision plays a central role. These stories illustrate the message that success is generally obtained by transgressing rules (metis) and frequently use mimesis as a means of bridging the gap with patients or hierarchical superiors. The author shows how these tales function as part of the defence strategies set in place by carers as they carry out their work. She compares and contrasts this with defence strategies used in high risk ‘male’ occupations which call upon gender stereotyped virility, while the stories described here are more generally used by groups of female carers, who do not forasmuch consider them to be an expression of femininity. Self-derision is a means of deflating the Ego, thereby allowing carers to confront the real and leave plenty of room for others to exist.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".