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Record W1545374761 · doi:10.1177/117718011401000403

Lessons for Collaboration Involving Traditional Knowledge and Environmental Governance in Ontario, Canada

2014· article· en· W1545374761 on OpenAlexaffabout
Deborah McGregor

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousScope (computer science)Corporate governanceGovernment (linguistics)Traditional knowledgeSustainabilityExploitPolitical scienceProject commissioningPublic relationsEnvironmental governancePublic administrationEconomic growthBusinessPublishingEconomicsEcologyLaw

Abstract

fetched live from OpenAlex

Efforts to incorporate Aboriginal traditional knowledge (TK) into environmental governance regimes in Ontario continue to evolve in urgency, scope and complexity. Both Indigenous and non-Indigenous parties have an interest in seeing such undertakings succeed, and yet errors in implementation continue to derail initiatives on a far too frequent basis. A brief look at the impetus behind current initiatives is provided, followed by highlights of some of the reasons why Aboriginal groups remain cautious in their interactions with outside agencies wishing to utilize and potentially exploit their knowledge. As well, reasons are offered as to why Indigenous peoples continue to see the sharing of TK as necessary in the move towards achieving global sustainability. Finally, lessons from two Ontario examples of attempts at government–First Nation collaboration are presented. Key among these is the finding that early, ongoing and mutually beneficial relationship-building between involved parties is essential to project success.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2014
Admission routes2
Has abstractyes

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