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Record W7056389492

Enhancing knowledge mobilisation and community engagement for research in Inuit Nunangat: A case study of participatory policymaking and collaborative research partnership in Nunatsiavut

2023· dissertation· en· W7056389492 on OpenAlexaboutno aff

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipParticipatory action researchCitizen journalismGovernment (linguistics)Work (physics)Community engagementKey (lock)Engaged scholarshipParticipatory GIS
DOInot available

Abstract

fetched live from OpenAlex

This thesis proposes a relational approach to enhance knowledge mobilisation and community engagement for research in Inuit Nunangat. The key principles of respect, mutual understanding, and researcher responsibility provide a foundation for effective knowledge mobilisation while acknowledging the need for context-specific adaptations. Using a case study, this work evaluates a collaborative research partnership with Nunatsiavut Government using participatory scenario planning for community-engaged policymaking. Participatory scenario planning is a robust tool for fostering hope, awareness, and collective responsibility. Furthermore, the evaluation underscores the importance of reconciliation, urging researchers to act in line with their values, be accountable to their relationships, and prioritise personal connections and trust-building. By adopting a relational approach and integrating these key principles, researchers can contribute to positive and ethically grounded knowledge outcomes while supporting Inuit self-determination in research.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.235
GPT teacher head0.405
Teacher spread0.170 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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