Is job evaluation compatible with care work?
Bibliographic record
Abstract
Using data from research on the undervaluing of predominantly female occupations, we found that the usual procedures for setting wages, notably job evaluation methods, may undervalue care work, which is predominantly done by women. Such work is difficult to analyze and evaluate because the current labor market is described by a static language of specialization and skills, whereas care workers should be judged more by their experience, which varies with the context and the situation. It is also difficult to appreciate and evaluate the true value of their work, which is sometimes invisible and often unquantifiable. According to Dejours and Gernet, care work relies on less noticeable abilities. A care worker must be able to anticipate another person's needs—an ability too often noticed only when absent—and be able to foresee, interpret, and understand the person's circumstances. The usual job evaluation methods seem confined to more objective and rational criteria.
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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 | 0.052 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".