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Record W4394282678 · doi:10.6084/m9.figshare.20847303

Working for Gender Equality in the Neoliberal Academia: Between Theory and Practice

2022· dataset· en· W4394282678 on OpenAlexaboutno aff
Barbara Poggio

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsGender equalitySociologyNeoliberalism (international relations)Political scienceGender studiesSocial science

Abstract

fetched live from OpenAlex

ABSTRACT In this executive letter, Barbara Poggio reports her experience as vice-rector of equality and diversity at the University of Trento (Italy), emphasizing the issue of gender asymmetries and the current challenges in the academic environment. The text results from a lecture at the Faculty of Business at the University of Lakehead (Canada) in June 2021. The letter’s purpose is to draw attention to the implication that the predominantly male academic environment has important implications for working conditions and quality of life. In addition, it highlights the impact of the current academic transformation and the affirmation of the neoliberal agenda, emphasizing performativity, excellence, and competition, and the implication in terms of increasing precariousness.

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)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.999
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0430.017

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 head0.446
GPT teacher head0.425
Teacher spread0.020 · 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.

Study designNot applicable
DomainIncentives
GenreDataset

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 routes1
Has abstractyes

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