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Record W3199149083 · doi:10.1080/0966369x.2021.1975102

The home-based postdoctoral mother in the neoliberal university

2021· article· en· W3199149083 on OpenAlexafffund
Christine Gibb

Bibliographic record

VenueGender Place & Culture · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAutoethnographySociologyWork (physics)InstitutionFamily and consumer scienceSocial mobilitySocial capitalPublic relationsGender studiesPolitical sciencePsychologySocial science

Abstract

fetched live from OpenAlex

For centuries, women have practiced home-based work. What happens when this strategy enters the academy? What happens when a postdoctoral fellowship becomes home-based work? Using autoethnography, I examine the home-based postdoc as one option sought by gendered subjects with care obligations and limited mobility for accruing academic capital. Through telecommuting and occasional visits, aspiring academic mothers gain valuable experience at prestigious institutions without incurring the immediate social and financial costs of uprooting their families. Yet women’s home-based academic work cannot provide the long-term professional and care benefits of on-campus presence. To probe the challenges and strategies of a home-based postdoctoral mother, I study three tensions: im/mobility, spaces of re/production and care. This ploy for decoupling geographic from social mobility and reconciling family care with career progression speaks to gendered labour relations and the work-life balance myth. The home-based postdoc, with its inherent compromises, is thus a perennial site of critique of the neoliberal academic institution.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.022
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.069
GPT teacher head0.274
Teacher spread0.204 · 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 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

Citations8
Published2021
Admission routes2
Has abstractyes

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