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Record W6999137333

BRIDGING INDIGENOUS KNOWLEDGE AND WESTERN SCIENCE: CO-CREATING BEST PRACTICES FOR COLLABORATIVE ENVIRONMENTAL RESEARCH

2024· dissertation· en· W6999137333 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
FundersIndigenous Services Canada
KeywordsIndigenousTraditional knowledgeBest practiceBridging (networking)Knowledge translationCommunity-based participatory researchData collectionCommunity engagement
DOInot available

Abstract

fetched live from OpenAlex

A co-creation framework was developed for non-Indigenous scientists and engineers aiming to conduct research with Indigenous communities. Developed from pre-existing CBPR and co-creation theories, this guide incorporated the personal experiences of two master's students working on this project. As Indigenous communities and individuals are not monoliths, the first draft of this framework was devised to be expanded for use with various other groups allowing researchers to apply relevant concepts specific to their projects. The co-creation framework was developed and executed by conducting an initial water quality analysis of drinking water from SN. Initiated by Duignan’s 2019 SN health survey feedback, preliminary water parameters were analyzed for select households across the community. Community services and members were instrumental in co-creating this style of data collection and knowledge translation with GWF researchers. Collections methods were primarily adapted due to the COVID-19 pandemic, in which researchers were led initially by community liaisons and taken to households to collect drinking water samples. Instead, homeowners were supported in collecting their own drinking water samples and providing them to community educators from SNHS. Concurrently, further development and application of the framework were established through an interactive video podcast, Ohneganos Let’s Talk Water, employed to conduct, disseminate, and translate relevant community research. The community-centred methodology met the target audience where they were, on social media, rather than expecting them to decipher conventional WS science dissemination methods such as academic conferences or peer-reviewed papers. International and transdisciplinary collaboration was explored between Indigenous and non-Indigenous youth, students, experts, artists and community members. This multifaceted, award-winning show was the first to combine these various elements. A mixed methods approach via digital story was produced to illustrate the impact of LTW. While an extensive variety of guests and topics were discussed in the four seasons of the podcast, the digital story highlights those most closely aligned with the work of this thesis, decolonizing western science research and dissemination.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.318
Teacher spread0.268 · 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 designOther design
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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