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Record W4388570623 · doi:10.33137/tijih.v1i3.38435

Furthering Anti-Racist Practice: Reconciliation in Action (RéconciliACTION) (Discussion Paper)

2023· article· en· W4388570623 on OpenAlexafffund
Jacqueline Avanthay Strus, Dave Holmes, Chad Hammond, Brianna Hammond

Bibliographic record

VenueTurtle Island Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversité de Saint-BonifaceUniversity of SaskatchewanUniversity of Ottawa
FundersWinnipeg Foundation
KeywordsTestimonialExperiential learningTransformative learningIndigenousSociologyScholarshipRacismReciprocity (cultural anthropology)PedagogyEngineering ethicsPolitical scienceGender studiesSocial scienceLaw

Abstract

fetched live from OpenAlex

Nursing scholarship and practice has been historically complicit in the (re)production of racial inequities by not acknowledging and countering their part in the legacy of colonization . This paper will discuss the implementation of an experiential transformative learning project, RéconciliACTION, grounded in critical social justice theory. Four elements – testimonial authority, experiential learning, reciprocity, and relationality - can be implemented in nursing education that value lived experience to create change toward address anti-Indigenous racism in educational settings and health institutions. Lessons from the RéconciliACTION Project reinforce the need to increase nursing educators' knowledge of such methods and practices. Essential to this process is the recognition of lived experience as knowledge via Testimonial Authority. The process of transformation begins with the integration of anti-racist practices and Indigenous content. This project seeks to create leaders and allies in the journey towards reconciliation, reducing anti-racist attitudes and practices in educational and medical facilities

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.401
Teacher spread0.344 · 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 teacher head, 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

Citations0
Published2023
Admission routes2
Has abstractyes

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