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Record W2338380956 · doi:10.14288/1.0076813

Capacity to manage water resources : perspective for the Sechelt Nation

2009· article· en· W2338380956 on OpenAlexaff
Sherry L. Boudreau

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerspective (graphical)BusinessEnvironmental resource managementEnvironmental planningComputer scienceEnvironmental scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The capacity to determine the future of any community depends on the extent of options available. This thesis describes the historical development of control over water resources, and how this impacts on the Sechelt Indian Bands (SIB) capacity to determine their future interests. Water management is a concern for the SIB both in terms of their being able to determine their future development, but also for non-consumptive issues such as maintenance of their fisheries. The construction of management authorities via legislation has curtailed the capacity for the SIB to define the development of Sechelt lands and participate in the management of water. The Sechelt Indian Band Self-Government Act was negotiated by the Sechelt people to expand control over the development of Band lands and resources. This municipal model of self-government, although it affords some benefits with respect to community access of water, is constrained by the continuance of licensed priority allocations, overlapping bureaucracies, and the Provinces focus on program (versus area) management. The Provincial the Chapman/Gray Integrated Watershed Management process, was reviewed to ascertain whether, by their participation in this process, the SIB has wrested any control over water and water management. The discussion highlights that: within the process extractive industries are still a priority, this can be a prohibitively protracted experience, and that the responsibility conferred on the water purveyor does not enable the authority to deliver quality water. This affects the Bands capacity to actualize their community vision.

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)0.001
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 score0.828
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.012
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.000

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 head0.009
GPT teacher head0.154
Teacher spread0.146 · 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
Published2009
Admission routes1
Has abstractyes

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