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Record W3044194369

Developing Participation and Understanding Through Community Engagement. Engaging with the Kitsumkalum Land Code Policy

2020· dissertation· en· W3044194369 on OpenAlexaboutno aff
Quinn Barabash

Bibliographic record

VenueMunin Open Research Archive (The Arctic University of Norway) · 2020
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCommunity engagementPolitical scienceCode (set theory)Civic engagementEnvironmental planningPublic relationsSociologyGeographyComputer scienceProgramming languagePolitics
DOInot available

Abstract

fetched live from OpenAlex

Kitsumkalum Nation is an Indigenous community located in Northwest British Columbia, Canada. They are working to redefine their relationship with the Canadian Government by pursuing a major policy change through Land Code. Kitsumkalum Nation realized that they needed to undertake community engagement strategies about the proposed Land Code policy change, with the goals of increasing community awareness of this complex technical issue and securing First Nations’ input into the decision-making process. This research, designed to contribute to the scholarly literature on community engagement processes, was based on the expressed desire of the Kitsumkalum Nation to determine the best way to communicate with community members. \nAfter several meetings with Kitsumkalum Nation staff, I conducted a literature review on community engagement. As a result of my preliminary research on this topic and extensive consultations with community leaders, the Kitsumkalum Nation decided to experiment with video communications as a method to share information more efficiently and to engage the community in discussions and decision making. Typical methods of communicating information to Kitsumkalum Band members, such as public meetings, have not met the Nation’s needs or expectations. They hoped that the shortcomings in earlier communications methods may be overcome in part through the use of video communications as a community engagement tool.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.227
GPT teacher head0.348
Teacher spread0.122 · 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 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
Published2020
Admission routes1
Has abstractyes

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