MétaCan
Menu
Back to cohort
Record W7052240632

Querying water co-governance: Yukon first nations and water governance in the context of modern land claim agreements

2020· article· en· W7052240632 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousContext (archaeology)Corporate governanceGeneral partnershipJurisdictionWater tradingWork (physics)Colonialism
DOInot available

Abstract

fetched live from OpenAlex

There exist few examples of functioning water co-governance systems where Indigenous and settler colonial governments work together to share authority for water on a nation-to-nation basis. In this paper I examine the multiple barriers to achieving water co-governance, highlighted by a multidimensional framework including distributional, procedural and recognitional (in)justices. I apply this framework to a case study in the Yukon, Canada, which is based on research conducted in partnership with four out of fourteen Yukon First Nations (Carcross/Tagish, Kluane, Tr'ondëk Hwëch'in and White River First Nations); all are in areas where the water governance system is shaped by Indigenous water rights and authorities that are acknowledged in modern land claim and selfgovernment agreements. Despite the many substantive and positive changes resulting from the explicit acknowledgement of Yukon First Nation water rights, I find that this system falls short of achieving co-governance. In particular, Yukon First Nations critiques highlight the limitations imposed by the continued assertion of 'Crown' jurisdiction over water and by the marginalisation of Indigenous legal orders that follows from the privileging of settler worldviews and forms of governance. Thus, co-governance arrangements depend not only on the distributional justice of shared jurisdiction; Indigenous legal orders and relationships to water must also be reflected in the procedural and recognitional justices of the decision-making processes and institutions that are developed.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.020
Scholarly communication0.0090.005
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.429
Teacher spread0.312 · 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 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

Citations21
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicPlasma Diagnostics and ApplicationsFrench-language works237,207