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Record W2791165880 · doi:10.1108/tlo-05-2017-0049

Universities as inclusive learning organizations for women?

2017· article· en· W2791165880 on OpenAlexaff
Patricia A. Gouthro, Nancy Taber, Amanda Brazil

Bibliographic record

VenueThe Learning Organization · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Prince Edward IslandBrock UniversityMount Saint Vincent University
Fundersnot available
KeywordsPrivilege (computing)Learning organizationSociologyOriginalityValue (mathematics)Higher educationPublic relationsOrganizational culturePedagogyWork (physics)Knowledge managementPolitical scienceSocial scienceEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore the concept of the learning organization, first discussed by Senge (1990), to determine if it can work as a model in the higher education sector. Design/methodology/approach Using a critical feminist framework, this paper assesses the possibilities and challenges of viewing universities as inclusive learning organizations, with a particular focus on women in academic faculty and leadership roles. Findings It argues that, ultimately, the impact of neoliberal values and underlying systemic structures that privilege male scholars need to be challenged through shifts in policies and practices to address ongoing issues of gender inequality in higher education. Originality/value The paper draws attention to the need to bring a critical feminist lens to an analysis of the concept of the learning organization if it is to be perceived as having merit in the higher education sector.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.017
Scholarly communication0.0150.011
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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 designQualitative
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

Citations13
Published2017
Admission routes1
Has abstractyes

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