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Record W4390769892 · doi:10.1108/edi-05-2023-0140

Examining the boundary conditions of tokenism: within-occupation gender wage gaps and female representation in the Canadian labor market

2024· article· en· W4390769892 on OpenAlexaboutno aff
Amber L. Stephenson, David B. Yerger

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsTokenismWageEarningsMale femalePoint (geometry)Percentage pointDemographic economicsOccupational segregationEconomicsLabour economicsPsychologySociologyMathematics

Abstract

fetched live from OpenAlex

Purpose The purpose of this study was to examine the boundary conditions of Kanter's (1977) tokenism theory as applied to the gender wage gap. The authors aimed to discover if there was a point where the relationship between the percentage of women in a job category and the gender wage gap changed, and, if so, where the threshold was located and what was the nature of the shift in relationship. Design/methodology/approach The authors used the Andrews’ (1993) threshold effects technique. Using 22 separate years of publicly available Canadian wage data, they examined the relationship between the percentage of females in 40 unique occupational categories and the female-to-male earnings ratio (for a total of 880 observations). Findings The results showed the existence of a threshold point, and that early gains in percent female within an occupation, up to approximately 14% female in the occupation, associate with strong gains in the female-to-male wage ratio. However, beyond that point, further gains in percent female associate with smaller improvements in the female-to-male wage ratio. Practical implications The findings are useful in understanding the dynamics of occupational group gender composition, potential theoretical reasons for the nuances in relationship, as well as opportunities that may facilitate more equitable outcomes. Originality/value The results show that, though improvements were made above and below the threshold point, enhancements in the wage gap are actually larger when there are less women in the job category (e.g. tokens).

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.306
Teacher spread0.226 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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Same venueEquality Diversity and Inclusion An International JournalSame topicLabor market dynamics and wage inequalityFrench-language works237,207