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Record W2209156475

“Melq'ilwiye” Coming Together: Reflections on the journey towards Indigenous social work field education

2014· article· en· W2209156475 on OpenAlexaboutno aff
Natalie Clark, Julie Drolet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIndigenous educationField (mathematics)SociologyStorytellingExploratory researchSocial workPedagogyNarrativePublic relationsPolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

This article shares the reflections, based on exploratory research and practice in the Interior of British Columbia (BC), Canada, of social work and human service field education coordinators on reconciling field education programs. Drawn from a larger study, the authors present the findings from in-depth interviews, using an Indigenous intersectional storytelling approach to understand the experiences of Indigenous and non-Indigenous field coordinators in moving towards an Indigenous field education model. There is limited research on Indigenous field education and few publications on the experiences of field education coordinators about this important area of practice. This article draws from the study's previous publications and focuses specifically on the narratives of field education coordinators in order to contribute to the development of new literature on the process of reconciling field education practices. The findings of the study call for a transformation of field education policies and practices in order to support Indigenous intersectional and culturally safe field education.

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)0.014
metaresearch head score (Gemma)0.019
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 score0.126
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0510.035
Scholarly communication0.0130.008
Open science0.0030.016
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0040.001

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 head0.099
GPT teacher head0.437
Teacher spread0.338 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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