Bibliographic record
Abstract
In the previous chapter I introduced a group of fathers whom I visited, usually on several occasions, while they were at home caring for their children. I used my observations during those visits, and their descriptions of their activities, to provide a cumulative picture of their caring work as embodied. My main focus, as I noted in concluding that chapter, was on what they were doing . The care they were engaged in was routine, and I argue that for that reason, it can be used as a benchmark; any father, taking on the engaged, hands-on care of a baby, will be bringing the physical capital of his (male) body to the job, and will learn (with few variations) the body techniques of caring that those fathers described. What remains to be explored is what this embodied care means to fathers who perform it. How do they describe the experience of taking time away from their paid employment to embark on this radically different kind of work? What effects do they perceive it to have had, on themselves as fathers, and as men? In this chapter, I move from my earlier focus on the ‘doing’ of care, to consider how it is experienced. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".