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Record W4311685094 · doi:10.22584/nr54.2023.001

Navigating the Shifting Landscape of Engagement in Northern Research: Perspectives from Early Career Researchers

2022· article· en· W4311685094 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Northern Review · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsYukon UniversityUniversity of Alberta
FundersUniversity of AlbertaAssociation of Canadian Universities for Northern Studies
KeywordsIndigenousReciprocity (cultural anthropology)Traditional knowledgeSociologyWork (physics)Community engagementRelevance (law)Public relationsColonialismKnowledge translationPolitical scienceSocial scienceEnvironmental ethicsEcologyEngineering

Abstract

fetched live from OpenAlex

Advance Online Article published December 16, 2022An examination of research in northern Canada and its ties to extractive, colonial practices has been highlighted in recent years, alongside heightened expectations for community- and Nation-engaged practises. Here, we explore the diverse ways that northern-focused early career researchers (ECRs), from a range of faculties, life experiences, and disciplines, engage with the communities and Indigenous Nations they work in and, more broadly, the knowledge they have gained from conducting research in the North. Scholars in the fields of education, anthropology, and renewable resources from the University of Alberta share their experiences to discuss 1) approaches to meaningfully and respectfully engaging with communities and Nations in the North; 2) knowledge translation and mutual capacity building; and 3) responsibilities and accountabilities for engaging with communities and Nations. We find resonance with the Five R’s of research—relevance, reciprocity, respect, responsibility, and relationship—that help ensure Western-derived knowledge benefits the communities and Nations that ECRs work alongside.

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)0.020
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
GPT teacher head0.486
Teacher spread0.182 · 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