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Record W2559790619 · doi:10.1177/1066480716679643

Remaking Our Identities

2016· article· en· W2559790619 on OpenAlexaffabout
Elise J. Matthews, Michel Desjardins

Bibliographic record

VenueThe Family Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsChildlessnessQualitative researchPsychologyResistance (ecology)Thematic analysisContext (archaeology)Identity (music)General partnershipSocial psychologyConstruct (python library)Sociocultural evolutionGender studiesDevelopmental psychologySociologyPopulationPolitical science

Abstract

fetched live from OpenAlex

Previous qualitative research findings have discussed motivations, decision-making, stigma, and resistance to pronatalism among voluntarily childless (VC) men and women. The current study placed such elements of the lifeworlds of VC individuals and dyads within the context of a life story of (re)making of the VC identity. Twelve life history and semistructured interviews with six VC men and women in three heterosexual couples in Canada were analyzed using thematic analysis. The VC choice was expressed as a decision to accept one’s essential voluntary childlessness. The construction of the VC participants’ bodies through their stories entailed episodes of conflict and resistance central to gendered experiences. We propose that this pattern of themes, in a pronatalist sociocultural context, points to a remaking of the figure of an extraordinary person from childhood, through to their current partnership, and into the future. These findings have implications for practitioners working with VC couples as they construct their identities, partnerships, reproductive decisions, life trajectories, and life projects.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.038
Scholarly communication0.0090.009
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.331
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations20
Published2016
Admission routes2
Has abstractyes

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