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Record W2128410145

Exploring Water Governance and Management in Oneida Nation of the Thames (Ontario, Canada): An Application of the Institutional Analysis and Development Framework

2013· article· en· W2128410145 on OpenAlexaffabout
Kate Cave, Ryan Plummer, Rob ``

Bibliographic record

VenueUNU Collections (United Nations University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsBrock UniversityUniversity of Waterloo
Fundersnot available
KeywordsCorporate governanceContext (archaeology)Political sciencePublic administrationEnvironmental planningEnvironmental resource managementSociologyBusinessGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Water is vital to Canada’s First Nations peoples. Despite the significance of water and ongoing efforts by various actors in Canada to make improvements, the conditions of drinking water safety are a persistent concern and deplorable in many First Nation communities. This research explores water institutions and their influence on water governance and management in a First Nations context. Oneida Nation of the Thames, located in southern Ontario, is the specific case investigated. This community has drinking water concerns and a myriad of institutions relating to water governance and management. The Institutional Analysis and Development (IAD) framework guided the exploration in Oneida. Water institutions (formal and informal) are identified and analysed in terms of exogenous factors, the action arena, patterns of interaction, and outcomes of these interactions. An evaluation of institutional performance in relation to water governance and management is offered. Gaining insights about how institutions guide the behavior of people involved in water governance and management in Oneida highlights the need to consider their influences in other First Nation communities.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.039
Science and technology studies0.0040.000
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 head0.021
GPT teacher head0.206
Teacher spread0.186 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations37
Published2013
Admission routes2
Has abstractyes

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