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Record W4413415944 · doi:10.1017/s1537592725102661

“Talkin’ ‘bout a Revolution”: Change for Care, and Care for Change

2025· article· en· W4413415944 on OpenAlexaff
Fiona Robinson

Bibliographic record

VenuePerspectives on Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Intersectionality
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Care theorists have had enough. Decades of neoliberalism, followed by financial crisis, austerity, gender backlash, and, in 2020, a worldwide infectious disease pandemic, have clearly tested their patience. The titles alone of three recent books on the ethics and politics of care suggest a change in tone; indeed, “radical,” “revolutionary,” and “manifesto” are generally not words we associate with the scholarship of those interested in the everyday practices of responding to the needs of others. And yet for Maurice Hamington, Lynne Segal, and co-authors Jennifer Nedelsky and Tom Malleson, these quotidian practices, and the ethos that underlies them, are more radical than they seem. Indeed, these volumes suggest that a commitment to care—a commitment that is both ideational/ethical and material—is necessary to usher in the kind of politics we so desperately need today. It could be, then, that with their latest books, these authors are edifying and formalizing what we might call the “radical turn” in research on care—a turn that can be roughly said to have begun in 2020 with the Care Collective’s The Care Manifesto (Verso) and the parallel Care Manifesto (Femnet) written by and for women of Africa, Asia, and Latin America a year later.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.022
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0220.105
Scholarly communication0.0220.020
Open science0.0020.011
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0110.001

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 head0.394
Teacher spread0.313 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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