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Record W7096198526

Atlantis, Volume 29.1, Fall/Winter 2004 Good Work in Canadian Childcare: Complicating the Love/Money Divide

2016· article· en· W7096198526 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsKindnessEconomic JusticeWork (physics)Social justiceSocial workAutonomy
DOInot available

Abstract

fetched live from OpenAlex

Extended interviews with thirty seven experienced and student childcare providers in Vancouver, British Columbia, provide data for an analysis of the love/money dichotomy often used to understand work in childcare. Providers talked both about the pulls between "money" and "love " and about their desire for other rewards from their work. This article uses ideas of workplace democracy and feminist social justice theories to help articulate how childcare work can be understood and supported as good work. RÉSUMÉ De longues entrevues avec 37 étudiantes et travailleuses de garderie chevronnées à Vancouver, en Colombie- Britannique, fournissent les données nécessaires pour faire l'analyse de la dichotomie entre l'amour et l'argent qui est souvent employée pour aider à comprendre le travail en milieu de garderie. Cet article se sert des idées de la démocratie en milieu de travail et de théories féministes sur la justice sociale pour aider à articuler comment le travail de garderie fonctionne et comment il peut être compris et appuyé en tant que travail appréciable. You know it's like, we do this because we love children but we don't do this totally out of the kindness of our heart. This is our job. This is our profession. We went to school for it. We paid dearly for it. We still pay dearly for it because we

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)0.001
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0270.009
Scholarly communication0.0090.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0390.002

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 head0.018
GPT teacher head0.262
Teacher spread0.243 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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