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Record W4387420615 · doi:10.1111/tct.13664

Commentary: Imagining possibilities for JEDI in research

2023· article· en· W4387420615 on OpenAlexaff
Laura Yvonne Bulk, Joanne Kerins, Neera R. Jain

Bibliographic record

VenueThe Clinical Teacher · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSociologyQualitative researchFraming (construction)ConsciousnessEmpirical researchMedia studiesEpistemologySocial science

Abstract

fetched live from OpenAlex

This is the final article in a three-paper series focused on enacting a justice, equity, diversity and inclusion (JEDI) lens in qualitative research. Article one described how to conduct research with a JEDI lens even when this is not the central research focus.1 The authors provided tools for awakening your critical consciousness and JEDI-advancing strategies for conceptualising the phenomenon under study, orienting the research paradigm and methods and analysing data. Article two reported an empirical study of internal medicine trainees that exemplified some of the strategies offered in article one.2 The authors demonstrated how trainees' social identities (gender and country of origin) are part of complex identity transitions. In this article, the authorship teams from articles one (Laura and Neera) and two (Joanne) unite to discuss how article two took up a JEDI lens and to explore how the research in article two might shift if the authors enacted alternative JEDI strategies. Our goal is to illuminate the possibilities that open through enacting a JEDI lens in research. We conclude by sharing how we have been transformed by this process and inviting readers to join in this transformation. Starting out in qualitative research, and even more so when seeking to research with a JEDI lens, can be challenging when we have been taught to think in positivist ways.14 We hope that readers will not be deterred by fear of not doing JEDI ‘right.’ Employing just one small strategy is a good start. We invite you to consider these concerns in the research you read, review and conduct. Table 1 offers some strategies to work through the challenges that may arise. As discussed in article one, this is a journey—we encourage a mindset of continual progress, not perfection.1 As the authors of this article, we are conscious that flaws remain in our own scholarship. We continually learn how to be, and work at being, better allies and co-conspirators in the JEDI arena. There are challenges structured into academic institutions where priorities may differ, but as a scholarly community, we can move this forward in a good way. Acknowledge complexity in the ways you analyse and write about data. Draw from theory and methods that help you to identify, appreciate and highlight nuance in your findings and interpretation. Disclose research team dissent.5 Admit limitations. Ask colleagues for feedback. Remember that language matters. Choose words carefully in consultation with others, check understandings and acknowledge that language is contested. Include rationale for language choices in your writing. Research unfamiliar terminology. Read widely and deeply, discuss your understandings with others and cite your guiding sources. Demonstrate your work: explain what you have done and why as well as the grappling you have engaged with. Acknowledge your mistakes. Be open about vulnerabilities among the research team with each other and in writing. Laura Y. Bulk: Conceptualization; data curation; investigation; methodology; project administration; resources; writing—original draft; writing—review and editing. Joanne Kerins: Conceptualization; data curation; methodology; project administration; resources; writing—original draft; writing—review and editing. Neera R. Jain: Conceptualization; data curation; methodology; project administration; resources; writing—original draft; writing—review and editing. The authors are grateful to the scholars, activists, learners and other individuals who have helped shape their critical thinking and learning. They thank Prof. Lara Varpio, Dr. Abby Konopasky and Dr. Katherine Schultz for including this topic in the Triptych series, the opportunity to learn together and for their support in editing these papers. With sincere gratitude, Laura and Neera acknowledge that they are settlers and are privileged to learn, play, work and live on Indigenous lands: Laura on the unceded, ancestral and continually occupied territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh Úxwumixw (Squamish), Tsleil-Waututh (Slay-wa-tuth) and W̱SÁNEĆ (Saanich) Peoples; and Neera recognises the tangata whenua of Aotearoa, in particular Ngāti Whātua Ōrākei. As settlers and tangata tiriti, they recognise their responsibility to address colonial injustices in and beyond health professions education. No conflicts to declare. The authors have no ethical statement to declare.

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)0.196
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1960.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.905
GPT teacher head0.773
Teacher spread0.132 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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