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Record W4299578288 · doi:10.25071/1929-8471.99

Mothering in the Remote Academy

2022· article· en· W4299578288 on OpenAlexaff
Maggie Quirt

Bibliographic record

VenueINYI Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsYork University
Fundersnot available
KeywordsInequalitySociologyPandemicGender studiesOrder (exchange)Work (physics)Balance (ability)Bridge (graph theory)Coronavirus disease 2019 (COVID-19)PsychologyPublic relationsPolitical scienceMedicineEngineeringBusiness

Abstract

fetched live from OpenAlex

In this article, I use Rachel Kadish’s feminist analysis in The Weight of Ink as a jumping off point to explore the experience of mothering in the academy during the pandemic. The structural gender inequalities that constrain opportunities for Kadish’s female characters will be familiar to women in the academy who have long struggled to achieve work-life balance under patriarchal conditions. I argue that such inequalities have persisted in the shift to remote teaching, and that the pandemic experience of mothers in the academy has been characterized by challenges related to both proximity and absence. This, in turn, has implications for the role academic mothers play in helping youth integrate effectively into their university classes and cohorts. I maintain that women’s traditional role as bridge builders can contribute to positive outcomes for youth, but institutions must establish equitable faculty workloads in order to support these efforts in a more systematic and structured manner.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.982
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0180.013
Scholarly communication0.0050.005
Open science0.0010.010
Research integrity0.0010.005
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 head0.088
GPT teacher head0.364
Teacher spread0.277 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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