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Record W1582144764 · doi:10.7202/1012837ar

Ethical foundations and principles for collaborative research with Inuit and their governments

2012· article· en· W1582144764 on OpenAlexaffvenueabout
Lawrence Felt, David Natcher

Bibliographic record

VenueÉtudes/Inuit/Studies · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of SaskatchewanMemorial University of Newfoundland
Fundersnot available
KeywordsArgument (complex analysis)Research ethicsEngineering ethicsPerspective (graphical)AmbiguitySociologyPolitical scienceDilemmaFace (sociological concept)Statement (logic)Environmental ethicsSocial scienceEpistemologyMedicineLawEngineering

Abstract

fetched live from OpenAlex

Academic research in Canada involving Aboriginal peoples has changed dramatically during the last 20 years. From an academic researcher’s perspective, the changes have recently become formalised in the release of the 2 nd edition of the Tri-Council Policy Statement on Ethics in Human Research. In this article we examine similarities and differences in the way ethical review is constructed and approached from university, Aboriginal and, in particular, Inuit perspectives. We begin our argument with a general comparison of research ethics as expressed in academic and Aboriginal sources in order to find areas of commonality, difference, and potential ambiguity between the two perspectives. We then briefly review our own experience with a multiyear research project involving several Inuit governments of different spatial and administrative scales. We conclude with discussion of a common issue arising from academic research, including our own work with Inuit and the research ethics board chaired by one of the authors. It concerns how to address potential tension between critical inquiry associated with Western scientific paradigms and respect and use of Inuit knowledge within a collaborative research process. In conclusion, we offer some “best practice advice” to academic researchers who face such a dilemma.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.332
GPT teacher head0.513
Teacher spread0.181 · 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.

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

Citations10
Published2012
Admission routes3
Has abstractyes

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