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Record W2340942939 · doi:10.1177/0038038515608129

Maternal Risk Anxiety in Belfast: Claims, Evaluations, Responses

2015· article· en· W2340942939 on OpenAlexfundno aff
Lisa Smyth

Bibliographic record

VenueSociology · 2015
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's University BelfastQueen's UniversityMcKnight Foundation
KeywordsSituatedSociologyNeighbourhood (mathematics)AnxietyPerspective (graphical)LoyaltySocial psychologyContext (archaeology)Gender studiesCriminologyPsychologyLawPolitical scienceHistory

Abstract

fetched live from OpenAlex

This article considers the social logic of maternal anxiety about risks posed to children in segregated, post-conflict neighbourhoods. Focusing on qualitative research with mothers in Belfast’s impoverished and divided inner city, the article draws on the interactionist perspective in the sociology of emotions to explore the ways in which maternal anxiety drives claims for recognition of good mothering, through orientations to these neighbourhoods. Drawing on Hirschman’s model of exit, loyalty and voice types of situated action, the article examines the relationship between maternal risk anxiety and evaluations of neighbourhood safety. In arguing that emotions are important aspects of claims for social recognition, the article demonstrates that anxiety provokes efforts to claim status, in this context through the explicit affirmation of non-sectarian 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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.438
Teacher spread0.352 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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