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Record W2914762077 · doi:10.1002/anzf.1347

Parents Making Meaning of High‐Conflict Divorce

2019· article· en· W2914762077 on OpenAlexaboutno aff
Rachel Treloar

Bibliographic record

VenueAustralian and New Zealand Journal of Family Therapy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)Meaning-makingAgency (philosophy)Context (archaeology)Interpersonal communicationTransformative learningNarrativeNegotiationConflict resolutionSocial psychologyProject commissioningPsychologyEmpirical researchSociologyPublishingPolitical scienceDevelopmental psychologySocial scienceLawEpistemologyPsychotherapist

Abstract

fetched live from OpenAlex

This article reports on the findings of an empirical study conducted with 25 parents in British Columbia, Canada, who experienced a high‐conflict divorce and later came to see the experience as having been transformative despite the difficulties they faced. While considerable research and policy initiatives frame high‐conflict divorce as an individual and interpersonal problem, there is less reference to the fact that these disputes occur in a social, political, and legal context that also changes over time and across generations. There has been little research examining long‐term divorce outcomes, and no research to date examining how mothers and fathers who experienced a high‐conflict divorce process overcome their difficulties and make meaning of their experiences retrospectively. This interdisciplinary study starts to fill these gaps. Following an overview of the study findings, the article highlights common themes arising from parents' narratives with a particular focus on agency, voice, and meaning‐making across the life course. I argue that by taking a long view of the challenges participants faced, it is possible to move away from decontextualised understandings of high‐conflict divorce.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

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

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 head0.335
Teacher spread0.249 · 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.

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

Citations27
Published2019
Admission routes1
Has abstractyes

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