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Record W2940431760

Leadership Development for Women Engineers in British Columbia, Canada

2019· book· en· W2940431760 on OpenAlexaboutno aff
Phyllis L MacIntyre

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsLeadership developmentCoachingLeadership styleEducational leadershipOperationalizationLeadership studiesNeuroleadershipEngineering educationInclusion (mineral)Political scienceServant leadershipManagementLeadershipPublic relationsPedagogyEngineering ethicsSociologyEngineeringSocial science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the quantitative correlational study was to profile the leadership of women engineers licensed in the province of British Columbia by using the Leadership Practices Inventory to operationalize leadership and explore associations with levels of university education, executive coaching, years of engineering practice, and the location of practice as rural versus urban. The number of women leaders continues to increase in Canadian corporations while the influence of women engineer leaders is not as progressive. Growth in the fields of engineering leadership education, management education, and leadership education offered sufficient evidence to pursue research that furthered the leadership development of women engineers. In university engineering education inclusion of leadership education improved, while attention to leadership development for professional women engineers remained sparse. Findings of this study described the leadership style of women engineers and suggested the combination of education and learning approach for a program in leadership development for women engineer leaders.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.166
Teacher spread0.155 · 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 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
Published2019
Admission routes1
Has abstractyes

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