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Record W2340041503

Potentials for Public Engagement in Source Water Protection in Newfoundland and Labrador: A Literature Review

2015· review· en· W2340041503 on OpenAlexaboutno aff
Sarah Minnes

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPublic engagementStewardship (theology)SustainabilityCorporate governanceEnvironmental planningPlan (archaeology)WatershedBusinessWater sourceCommunity engagementPublic participationPolitical scienceEnvironmental resource managementWatershed managementPublic relationsGeographyWater resource managementEnvironmental sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

This literature review explores the role of citizen engagement in watershed planning, governance, and management, and more specifically the implications for increased citizen engagement in source water protection efforts in Newfoundland and Labrador. This is particularly of concern for rural Newfoundland and Labrador, which suffers from a lack of capacity to adequately manage source water supplies that contribute to their drinking water systems. It has been found in other areas of Canada and beyond that increased citizen engagement can have a myriad of benefits for watershed stewardship in general, and can help to address the lack of human and financial capacity to sustainably plan, govern and manage source water supplies. Potentials for more opportunities for public engagement and better methods of public engagement in source water protection have been provided, according to the literature, as well as potential areas for future research related to this topic.

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)0.003
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.013
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.298
Teacher spread0.220 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2015
Admission routes1
Has abstractyes

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