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Record W7046669869

Crashing the Boards: A Comparative Analysis of the Boxing Out of Women On Boards in the United States and Canada

2019· article· en· W7046669869 on OpenAlexaboutno aff

Bibliographic record

VenuePepperdine Digital Commons (Pepperdine University) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)NormativeJurisdictionLegislationExecutive branch
DOInot available

Abstract

fetched live from OpenAlex

This paper will first provide a critical, comparative look at the Canadian and the federal American responses to the under-representation of women on boards of large, publicly traded corporations. There will be a discussion about the competing conceptions which emerge in addressing the regulation of women on boards in the United States and Canada and why each jurisdiction implemented its policy when it did. The conceptions arising out of questions about under-representation of women on boards tend to fall within two categories: business case rationales and normative rationales. Given the competing conceptions of this issue, this paper will attempt to demonstrate how the regulatory regimes fit within these conceptions and the solutions which follow each conception. An argument will be advanced that not only does each disclosure regime fail to provide a solution to the underlying issue it is attempting to regulate, but also neither regime even advances the goal the regulators purport to be advancing. Finally, a closer look at the polarizing reactions to Bill 826 provides a hint as to the future direction of the American and Canadian debates. This paper will be one of the first to discuss Bill 826 and what it may mean for the U.S. and Canada.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.253
Teacher spread0.211 · 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.

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
Published2019
Admission routes1
Has abstractyes

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