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Record W2889871056 · doi:10.1080/09589236.2018.1515068

Broads with rods: the social world of female fly anglers

2018· article· en· W2889871056 on OpenAlexaff
David A. Fennell, Meghan Birbeck

Bibliographic record

VenueJournal of Gender Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock University
Fundersnot available
KeywordsHabitusSnowball samplingIdentity (music)PerformativitySociologyEmpowermentGender studiesRecreationSocial psychologyIdeologyPsychologyCultural capitalSocial sciencePoliticsPolitical science

Abstract

fetched live from OpenAlex

Bourdieu’s theory of habitus was used to determine if a comprehensive identity exists amongst female fly anglers. Past research has emphasised a need to address ‘doing gender’ and ‘gender performativity’ in sport and recreation to understand ideology surrounding male superiority and the marginalisation of women. Fly fishing is a traditional male-dominated and masculine sport, where women are slowly emerging as prominent figures. Fly fishing presents a setting to then understand the performance of gender and the influence of social norms. A snowball sample of female fly anglers (n = 63) was obtained from an online survey, which was administered between December 2015 and January 2016. Descriptive statistical analysis of a structured closed-category online survey was used to determine if a distinct symmetry and set of practices exist in defining the identity of female fly anglers. Results indicate that a separate habitus is emerging for these women built around adventure, being in nature, identity, freedom, lack of guilt, commitment, empowerment, independence, anti-control and anti-domination, and the maintenance of stereotypical feminine characteristics through participation in this activity.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.187
GPT teacher head0.429
Teacher spread0.242 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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