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Record W4213263618 · doi:10.32920/ryerson.14646294.v1

Male social workers: the experience of masculinity in "female" work

2021· preprint· en· W4213263618 on OpenAlexaff
Michael Dionisi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityLakehead UniversityCentre for Social Innovation
Fundersnot available
KeywordsPatriarchyHegemonic masculinityMasculinityGender studiesNarrativeContext (archaeology)Social workSocial positionSociologyFace (sociological concept)HegemonyResistance (ecology)Social psychologyPsychologySocial relationPolitical sciencePoliticsSocial science

Abstract

fetched live from OpenAlex

This MRP explores the experiences of male social workers working within the female dominated profession of child protection. More specifically, it was the goal to examine a) the potential obstacles that male social workers face as a result of hegemonic constructions of masculinity; b) whether these men use their position as a site of resistance to the patriarchal social order; and c) whether workplace dynamics further perpetuate patriarchy. Using the narrative approach three male social workers were interviewed. These co-researchers indicated that they experienced difficulties building rapport and engaging with clients, and expressed challenges regarding how they are perceived. They also explained that being a male social worker can have some privileges when it comes to interacting with male clients and being given more authority in their roles. When taken together, it becomes clear that patriarchal ideals and assumptions are alive and well within the context of child welfare.

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 score0.015
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.014
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0020.003
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 head0.091
GPT teacher head0.409
Teacher spread0.318 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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