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Record W4372227802 · doi:10.34074/thes.revw12015

Thesis Review: Dis/identifications and Dis/articulations: Young Women and Feminism in Aotearoa/New Zealand

2015· report· en· W4372227802 on OpenAlexaboutno aff
Hélène Connor

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaFeminismPremiseGender studiesContext (archaeology)SociologyOriginalityValue (mathematics)Social scienceGeographyQualitative researchEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

In this thoroughly researched, skillfully written thesis, the author explores young women’s dis/identifications with feminism, and dis/articulations of feminism, within contemporary Aotearoa/New Zealand. The premise of the research is that whilst many young women value the work of the early feminists in terms of gender equality and individual freedom for themselves, only a small number position themselves as feminist. Indeed, the author identified research with young women in the United Kingdom, the United States, Germany and Canada which supported this premise. Comparative research on young women’s identifications with feminism in Aotearoa/New Zealand, was, however, absent within the literature and this thesis set out to address this gap. Overall, the thesis addresses the New Zealand context with considerable scholarly integrity and depth, demonstrating originality and a well-considered analytical response to the data.

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)0.007
metaresearch head score (Gemma)0.026
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 score0.129
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0030.003
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.081
GPT teacher head0.368
Teacher spread0.287 · 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
Published2015
Admission routes1
Has abstractyes

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