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Record W6983649676

Municipal-Indigenous collaboration in climate planning

2023· dissertation· en· W6983649676 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
FundersUniversity of Memphis
KeywordsIndigenousCorporate governanceClimate changeClimate governanceAction (physics)Psychological resilienceClimate resilienceUrban resilience
DOInot available

Abstract

fetched live from OpenAlex

Climate policy offers an ideal site to build collaborative governance relationships between Canadian municipalities and Indigenous peoples because climate action and decolonization both require a fundamental reworking of settler governance systems. However, few municipal climate action plans show evidence of relationships with Indigenous peoples. This research examines references to Indigenous peoples in published climate action plans from Canadian municipalities of over 20,000 people and urban regional governments. Through a mixed-methods content analysis covering the 139 municipalities and regions with climate action plans, this study finds that only half (69) make any reference to Indigenous peoples, and only about one in five (30) have ongoing working relationships. In most of these 30 ongoing relationships, Indigenous contributions to municipal climate plans are confined to certain topics or defined by engagement processes that ask for input without recognizing Indigenous authority. Truly addressing climate change requires increasing resilience for all people, which necessitates respect for Indigenous self-determination. The goal of this research is to serve as a jumping-off point for the establishment of decolonial partnerships between municipalities and Indigenous peoples.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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.

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
Published2023
Admission routes1
Has abstractyes

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