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Record W7047899564

Indigenous Water Co-Governance: Emerging Models of Distributed Water Governance in British Columbia and Alberta

2019· other· en· W7047899564 on OpenAlexaffabout

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2019
Typeother
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsFirst Nations University of Canada
Fundersnot available
KeywordsNucleofectionTSG101ProteogenomicsHyporeflexiaArticular cartilage damageGestational period
DOInot available

Abstract

fetched live from OpenAlex

The emphasis on Indigenous law is of pressing importance given that evolving legal \nframeworks have created expanded approaches to Indigenous title, rights, and traditional \nterritories and hence expanded roles for Indigenous peoples in resource governance. This \ncreates a challenge for all levels of government (including Indigenous governments), as new \nmodels of governance (and stakeholder relationships) are being debated and indeed \ncreated. \nThis challenge has inter-related economic, policy and governance dimensions. Indigenous \ncommunities in Canada are currently grappling with a range of water-related issues, \nincluding access to safe drinking water, environmental water quality, and associated health \nand livelihoods issues. In some regions, particularly Western and Northern Canada, these \nissues are exacerbated by development pressures associated with resource extraction (e.g. \noil and gas development, forestry, hydro-electricity). In this context, there are number of \nchallenges that stem from legal and regulatory frameworks, including inadequate \nconsultation, lack of community capacity to participate in engagement and consultation \nprocesses, insufficient transparency, and outdated regulations (e.g. with respect to new \npollutants) and perceived regulatory capture. \nIn the absence of effective responses to these challenges, there are a number of potential \nconsequences, including expensive and protracted litigation, higher appeals to (and thus \nincreased caseloads for) regulatory oversight bodies, and political mobilization and protest.

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)0.001
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: none
Teacher disagreement score0.130
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0090.009
Scholarly communication0.0090.002
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.005
GPT teacher head0.180
Teacher spread0.174 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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