MétaCan
Menu
Back to cohort
Record W4207057590 · doi:10.32920/16879081.v1

Becoming A Holistic Social Worker: An Autoethnography

2021· preprint· en· W4207057590 on OpenAlexaff
Sue Lam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsAutoethnographySociologyAppropriationGender studiesMental healthFeminismSocial workPsychologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

This research project is an autoethnography about my journey in becoming a holistic social worker. The theoretical framework includes critical race feminism, Asian feminism, and yin yang theory. The literature review finds social workers are integrating alternative healing practices in their professional work. The research methods are the process and product of writing the autoethnography. The research question is: what persons, situations, and experiences were influential in my becoming a holistic social worker? The findings indicate the influencing factors: family environment, learning kung fu and Chinese medicine, social justice activities at church, healing professionals, working in mental health field, training in counselling and mental health, and holistic social workers. The discussions indicate white supremacy, patriarchy, capitalism, and cultural appropriation had negative impacts on my life and presented opportunities for resistance.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.173
GPT teacher head0.458
Teacher spread0.285 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSocial Work Education and PracticeFrench-language works237,207