MétaCan
Menu
Back to cohort
Record W2123089587 · doi:10.1177/117718011401000505

Traditional Knowledge and Water Governance: The ethic of responsibility

2014· article· en· W2123089587 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
KeywordsCorporate governanceGovernment (linguistics)Political scienceEnvironmental ethicsRelation (database)Traditional knowledgeTreatyNarrativeSociology of scientific knowledgePublic administrationOrder (exchange)Environmental resource managementPublic relationsSociologyBusinessLawSocial scienceEcologyEconomics

Abstract

fetched live from OpenAlex

This paper is based on traditional knowledge policy research undertaken over the last 15 years with First Nations in Ontario. First Nations traditional knowledge-based responses to the water crisis evoke an alternative narrative to the dominant discourse. Canadian governments are focused primarily on scientific and technological approaches to resolving water quality issues. In contrast, First Nations are concerned mostly with the recognition of Aboriginal and treaty rights in relation to water. Application of such rights, as expressed by Elders and other traditional knowledge holders, leads to a much more holistic approach to water governance, one that involves fulfilling inherent responsibilities to ensuring water is protected. An overview of key elements of traditional knowledge as they relate to water governance and protection is provided. These are contrasted with highlights of Canadian government responses to water quality concerns across Canada. In order for progress to be made in the future, a nation-to-nation approach between Canadian governments and First Nations is needed.

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)0.009
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.110
Scholarly communication0.0140.008
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.390
Teacher spread0.333 · 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.

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

Citations145
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicIndigenous Studies and EcologyFrench-language works237,207