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Record W2800837525 · doi:10.5931/djim.v14i0.7873

Turning Exceptions into the Rule: Bridging the Gender Gap in Corporate Boardrooms and the C-Suites of Canadian Corporations

2018· article· en· W2800837525 on OpenAlexaffvenueabout
Justin Zuccon

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBridging (networking)Bridge (graph theory)Representation (politics)AccountingBusinessSubject (documents)Law and economicsPolitical sciencePublic relationsLawSociologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

This paper contextualizes the under-representation of women on the boards and in the executive suites of publicly-listed Canadian corporations by reviewing recent studies and statistics on the subject, dispelling traditional justifications for the gender imbalance and critically examining commonly proposed strategies to bridge the gap between policy and reality. Unfortunately, the current “comply or explain” regime was found to be ineffective. The author recommends that the provincial securities regulators band together to institute a mandatory quota system.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.081
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0210.014
Scholarly communication0.0080.003
Open science0.0020.006
Research integrity0.0010.002
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 head0.091
GPT teacher head0.308
Teacher spread0.217 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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