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Record W4386310004 · doi:10.56687/9781447365648-012

Ideal, good enough and failed motherhood: how disabled Canadian mothers manage in hostile circumstances

2023· book-chapter· en· W4386310004 on OpenAlexaboutno aff
Claudia Malacrida

Bibliographic record

VenuePolicy Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsIdeal (ethics)NormativeSanctionsWork (physics)PsychologySociologySocial psychologyPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

Ideal motherhood, characterised by expectations of intensive, limitless and selfless care, serves to hold women responsible for all that befalls their children, and works to individualise and privatise the work of childbearing and rearing. The individualisation of mothering work, the shortcomings of social and structural supports for that work, and the sanctions on women who fail to meet normative expectations of ideal motherhood are intensified for disabled women. Our narrative interviews with 44 disabled Canadian women about their pregnancy decisions and mothering experiences illuminate normative notions of what kinds of women can, and perhaps should, be mothers, and highlight the specific challenges disabled mothers face. The women described barriers to becoming and remaining mothers, and were particularly vulnerable to social isolation, abusive partners, and the effects of poverty. They also experienced surveillance and intervention from helping professionals and multiple structural barriers to accommodation. The women’s stories highlight ‘disability embodiment’, the interaction between their corporeal issues and the social, political and economic aspects of disability, which deeply affect disabled women’s mothering.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.014
Scholarly communication0.0090.004
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.093
GPT teacher head0.336
Teacher spread0.243 · 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
Published2023
Admission routes1
Has abstractyes

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