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Record W4376849869 · doi:10.32920/cd.v6i3.1691

Schooling of Peter Pan

2023· article· en· W4376849869 on OpenAlexafffundvenueabout
Phillip Joy, Lauren Hawthorne

Bibliographic record

VenueJournal of Critical Dietetics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsMount Saint Vincent University
FundersMount Saint Vincent University
KeywordsThematic analysisHeteronormativityComicsDiversity (politics)FeelingSexual orientationQualitative researchPsychologySpace (punctuation)Health careSociologyMedical educationPedagogyPublic relationsSocial psychologyGender studiesMedicinePolitical scienceQueerSocial science

Abstract

fetched live from OpenAlex

Purpose: The purpose of this research was to explore the knowledge, beliefs and experiences of Canadian dietitians relating to gender and sexual orientation diversity within the profession. Our aim is to share some of the knowledge, beliefs and experiences though comic art, a medium that is widely popular and has been used in health knowledge translation to teach about health, health practices and client care practices. Methods: The research conducted was a qualitative poststructural thematic discourse analysis. Sixteen Canadian dietitians were recruited and asked to share their thoughts and experiences and gender and sexual diversity in the profession. Results: Participants spoke of cis-heteronormativity, feelings of not belonging and offered suggestions to create a more inclusive profession. Through a creative collaboration with an artist, a comic was created to share these findings. Conclusion: Dietetic students, educators and practitioners must take an active role in seeking out and modelling learning opportunities to ensure that the profession continues to move toward ensuring a safe and inclusive space for 2SLGBTQIA+ individuals.

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)0.002
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.280
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2800.082

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 head0.109
GPT teacher head0.486
Teacher spread0.377 · 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
GenreEditorial

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

Citations3
Published2023
Admission routes4
Has abstractyes

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