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Record W6981830483

Flexible Enough? The Gender Gap and the Uptake of Flexible Work

2022· other· en· W6981830483 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2022
Typeother
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of TorontoGovernment of Canada
KeywordsFlexibility (engineering)Work (physics)AccommodationEquity (law)Gender gapVariable (mathematics)Gender pay gapWork–life balance
DOInot available

Abstract

fetched live from OpenAlex

Flexible work arrangements (such as remote work, variable scheduling, variable work hours and shorter work weeks or hours) have been identified by researchers as a remedy to the pressures of traditional gender roles (such as child rearing and housekeeping) that make it harder for female workers to reach equity with their male counterparts. However, as our report notes, although women disproportionally bear the brunt of work/life responsibilities, they report having less flexibility than men in changing the rate at which they work, as well as their working hours. This lack of flexibility is more pronounced for women with young or several children. Traditionally these pressures have resulted in women being over-represented in the accommodation and food services industry. Unfortunately, this industry has also been hit particularly hard by the COVID-19 pandemic. Canada may therefore have an unprecedented opportunity to expand employment opportunities for women by taking advantage of the widespread changes to work arrangements as a result of COVID-19. Our report provides a roadmap for policymakers and employers with multiple recommendations to extend options for flexible work across all industries. These include having employers implement a “trust-based” model to evaluate their employees on their output instead their hours, and creating informal networking opportunities for remote workers.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.573
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0470.000

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 head0.076
GPT teacher head0.373
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes2
Has abstractyes

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