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Record W2598408517 · doi:10.2166/wp.2017.078

Analysis of challenges and opportunities to meaningful Indigenous engagement in sustainable water and wastewater management

2017· article· en· W2598408517 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of Guelph
KeywordsIndigenousSanitationStatus quoBusinessSustainabilityCommunity engagementWastewaterEnvironmental planningAutonomyEnvironmental resource managementPolitical sciencePublic relationsEngineeringEconomicsGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Access to safe drinking water and adequate sanitation continue to be significant issues affecting Indigenous populations worldwide. The full participation of Indigenous peoples within water and wastewater policy and decision-making has been hindered by many factors, including capacity, inadequate resources and, overall, a lack of respect or formal recognition of Indigenous rights. This study investigates limitations to engagement around water and wastewater management and policy. Findings from this study show that in order to improve engagement with Indigenous people on water and wastewater management policy, systemic issues need to be addressed, in addition to gaining a greater understanding of the specific socio-economic conditions, and technical and financial capacity gaps, and the recognition of inherent Indigenous rights is necessary. It is concluded that long-term sustainability of water and wastewater management necessitates Indigenous engagement from the start, as well as increased autonomy over the management of their systems, including financing. The findings from this paper can be used by policy-makers and decision-makers to address the urgent issue of access to safe drinking water and sanitation, by improving the level of engagement with community members, and challenging the status-quo of top-down approaches through community-driven processes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.327
Teacher spread0.274 · 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