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Record W2130690872 · doi:10.1080/13668800802009422

Women's provisioning work: counting the cost for women living on low income

2009· article· en· W2130690872 on OpenAlexafffundabout
Stephanie Baker Collins, Sheila M. Neysmith, Elaine Porter, Marge Reitsma-Street

Bibliographic record

VenueCommunity Work & Family · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsUniversity of VictoriaLaurentian UniversityUniversity of TorontoYork University
FundersUniversity of TorontoYork University
KeywordsProvisioningBusinessWork (physics)Public relationsContext (archaeology)Care workMarketingPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper reports on a research project that uses the concept of provisioning as a starting place in understanding the activities women in marginalized communities undertake to provide for themselves and members of their households and neighborhoods. This project explores the household and collective provisioning undertaken by women who are all part of formal community organizations in Canada. The work women do is explored from the dimension of women's relationships of responsibility. This vantage point uncovers a complex web of activity including paid employment, voluntary work, care work, exchanges of goods and services, community work, and self-provisioning. In addition, the provisioning strategies that women use when public resources are scarce are explored. In the face of significant cutbacks in public provision of goods and services, women are engaging in a complex network of activities in order to compensate through private provisioning for resources that are no longer available through public provisioning. The policy context in which these strategies are pursued is explored as well as the way in which risky policies produce risky coping strategies.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.003
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.097
GPT teacher head0.364
Teacher spread0.267 · 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 designObservational
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

Citations7
Published2009
Admission routes3
Has abstractyes

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