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Record W3135173737 · doi:10.3390/w13050709

Beyond Institutional Ethics: Anishinaabe Worldviews and the Development of a Culturally Sensitive Field Protocol for Aquatic Plant Research

2021· article· en· W3135173737 on OpenAlexafffundabout
Brittany Luby, Samantha Mehltretter, Robert L. Flewelling, Margaret Lehman, Gabrielle Goldhar, Elli Pattrick, Jane Mariotti, Andrea Bradford

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Bank of Canada
KeywordsIndigenousHarmResearch ethicsField researchEnvironmental ethicsPolitical scienceSociologyEnvironmental resource managementEngineering ethicsLawSocial scienceEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2) guides knowledge production and dissemination in Canada. While it is intended to protect vulnerable populations from harm, it fails to consider Anishinaabe worldviews and, by extension, to effectively direct ethical water research with aquatic plant life. Using Anishinaabe oral testimony and oral stories, Niisaachewan Anishinaabe Nation (NAN) and the University of Guelph (UofG) co-developed a culturally sensitive field protocol to respect Manomin (Wild Rice) as an other-than-human being and guide research into Manomin restoration. By illuminating key directives from NAN, this article showcases the limitations of institutional ethics in Canada. It concludes with recommendations to broaden TCPS2 to better address Anishinaabe teachings about plant and animal relations, but ultimately challenges institutional Research Ethics Boards (REBs) to relinquish control and respect Indigenous Nations’ right to govern research within their territories.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.350
metaresearch head score (Gemma)0.337
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.421
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3500.337
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.006
Science and technology studies0.0200.032
Scholarly communication0.0160.007
Open science0.0060.010
Research integrity0.0110.024
Insufficient payload (model declined to judge)0.0080.003

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 head0.076
GPT teacher head0.348
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreMethods

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

Citations8
Published2021
Admission routes3
Has abstractyes

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