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Record W2981370558 · doi:10.1111/cfs.12709

The construction of maternal identity among Israeli mothers who are welfare clients

2019· article· en· W2981370558 on OpenAlexaff
Hagit Sinai‐Glazer, Maya Lipshes‐Niv, Einat Peled

Bibliographic record

VenueChild & Family Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsIdentity (music)NegotiationWelfareConstruct (python library)Vulnerability (computing)Context (archaeology)Social psychologyPsychologyHegemonyGender studiesDevelopmental psychologySociologyPolitical sciencePoliticsGeographySocial science

Abstract

fetched live from OpenAlex

Abstract Dominant social, cultural, and professional discourses view motherhood as the core of a woman's identity. Thus, the identity of women who do not meet the general expectations of motherhood might be negatively affected. Specifically, mothers who are welfare clients may violate the prevailing norms concerning the maternal role. This qualitative study explores how Israeli mothers who are welfare clients construct their maternal identity. Studies of mothers who are clients of welfare services suggest that they feel blamed for being “bad” or “unfit” mothers. However, women occasionally negotiate or challenge the negative perceptions that are forced on them by hegemonic discourses of motherhood. We postulate that such women experience a troubled maternal identity that they negotiate through interactions with others. Accordingly, this study set to explore how Israeli mothers who are welfare clients negotiate their maternal identity in the context of motherhood and welfare discourses. Fourteen participants were interviewed, and the findings illustrate the vulnerability of the mothers and their strategies to construct a positive maternal identity. We discuss the findings in light of the concept of troubled identity .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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 teacher head, not a consensus.

Study designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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