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Record W2795528191 · doi:10.1108/gm-06-2017-0073

Pay equity and marginalized women

2018· article· en· W2795528191 on OpenAlexaffabout
Roopkiran Kohout, Parbudyal Singh

Bibliographic record

VenueGender in Management An International Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsYork University
Fundersnot available
KeywordsLegislationThematic analysisOriginalityEquity (law)Gender pay gapJurisdictionQualitative researchSociologyPublic relationsPay EquityValue (mathematics)Political scienceEconomicsSocial scienceLabour economicsLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the experiences of marginalized women in achieving equal pay for work of equal value. The research focuses on Ontario, Canada, as this is a leading jurisdiction globally in implementing legislation on pay equity. It provides an opportunity to understand the lived experiences of women whom scholars have identified as particularly vulnerable in workplaces. Design/methodology/approach This is a qualitative research study. Twenty-three interviews were conducted with women defined as marginalized. Thematic analysis was used to analyze the data. Findings Three themes resulted from the analysis: early employment experiences, cultural challenges at work and inequities in pay. The authors found that not only do structural and organizational barriers limit the ability of marginalized women to achieve parity in the workplace but there also is a hidden social element that requires further investigation. Originality/value The gender pay gap is wider for marginalized women, even after three decades since pay equity legislation was implemented in Ontario. There is a dearth of research on why this is the case. This study adds to the literature by focusing on a broader set of factors, in addition to legislation, that must be considered when focusing on solutions to the gender pay gap.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.180
GPT teacher head0.393
Teacher spread0.213 · 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 teacher head, not a consensus.

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

Citations8
Published2018
Admission routes2
Has abstractyes

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