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Record W4414806344 · doi:10.1177/15579883251381426

Men’s Experiences with Fatherhood in Heterosexual Relationships: A Narrative Analysis

2025· article· en· W4414806344 on OpenAlexaff
Nina Gao, Christy Chan, Francine Darroch, Alex Broom, Sarah McKenzie, Jennifer J. Mootz, John L. Oliffe

Bibliographic record

VenueAmerican Journal of Men s Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsNarrativeReflexivityNarrative inquiryMasculinityPhotovoiceScholarshipHeteronormativityIdentity (music)

Abstract

fetched live from OpenAlex

The restructuring of gender identities and shifting social expectations of fathers have resulted in increased interest in understanding contemporary discourses about involved fathering. This article contributes to that scholarship by providing a narrative analysis about men’s experiences with fatherhood in heterosexual relationships. Drawing from a photovoice study with 16 fathers, we report three discrete but interrelated narratives. The first narrative Sacrifices in being a provider featured fathers’ diverse alignments to traditional masculinities as well as investments in contemporary involved fathering. The second narrative Balance in caring encompassed fathers’ efforts for adapting to ever-changing parenting responsibilities, and strategies for preserving intimate partnerships and a sense of self amid parenting demands. The final theme, Liberation of contemporary father identities , described participants’ reflexive identity [re]construction of masculinity in experiencing fatherhood. These findings highlight a matrix of modern-day fathering expectations and fathers’ relational practices in navigating shifting gender relations and masculinities.

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)0.004
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 score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.027
GPT teacher head0.368
Teacher spread0.342 · 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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