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Record W4402589953 · doi:10.1111/tct.13806

How to … do decolonial research

2024· article· en· W4402589953 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Clinical Teacher · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

The imperative for decolonial research in health professions education (HPE) is rooted in a resistance to coloniality, which characterises modern medicine and HPE. Coloniality is a residual effect of colonialism, which upholds White, Western, Eurocentric knowledge systems while simultaneously marginalising diverse epistemologies. We outline the problematic nature of coloniality in HPE typified in unequal research partnerships, skewed student exchanges and poor representation of diverse authors. Decoloniality advocates for the active disruption and dismantling of colonial hierarchies to promote epistemic justice. We suggest a practical framework for applying decolonial principles in research, emphasising awareness (critical consciousness), deliberation (reflexivity) and action (transformative praxis). Practical steps for decolonial practice include interrogating research conceptualisation, sharing power and diversifying research teams, adopting participatory and reciprocal (mutually beneficial) methodologies, (re)centring marginalised voices and amplifying 'Other' knowledges, and disrupting hegemonic dissemination practices. By employing decolonial strategies, researchers can produce equitable, socially accountable and epistemically just scholarship, ultimately enhancing the relevance and impact of HPE research for all people globally.

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.

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)0.132
metaresearch head score (Gemma)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1320.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.790
GPT teacher head0.745
Teacher spread0.045 · 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