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Record W4400915301 · doi:10.1177/00221856241254141

Is job evaluation compatible with care work?

2024· article· en· W4400915301 on OpenAlexaff
Yves Hallée, Annick Parent‐Lamarche, Miguel Delattre

Bibliographic record

VenueJournal of Industrial Relations · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité Laval
Fundersnot available
KeywordsWork (physics)Context (archaeology)Value (mathematics)Care workJob marketPsychologyLabour economicsEconomicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.222
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.222
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0040.011
Scholarly communication0.0100.009
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.237
GPT teacher head0.469
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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