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Record W7023650355

Perspectives on Indigenous knowledge governance in collaborative environmental stewardship

2023· dissertation· en· W7023650355 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsStewardship (theology)IndigenousTraditional knowledgeEnvironmental governanceCorporate governanceEnvironmental stewardshipCitizen journalismParticipatory action researchIndigenous rights
DOInot available

Abstract

fetched live from OpenAlex

Growing from inherent rights to steward territories, the weaving of Indigenous knowledge into environmental stewardship is increasingly being acknowledged and mandated for, both in Canada and internationally. The deep settler colonial roots of environmental stewardship and resource management in Canada, as well as the violence enacted on communities within these spaces and through resource management practices, make this a contentious and deeply complicated task. Furthermore, engagement with Indigenous peoples and their knowledge systems has historically been, and continues to be, extractive, dismissive, and paternalistic, disrupting Indigenous ways of being and failing to recognize inherent rights. In tandem with environmental stewardship rights, Indigenous peoples have articulated and asserted their inherent right to govern their knowledge and data. Indigenous knowledges come from and are practiced on lands and waters and, as such, Indigenous knowledge governance and environmental stewardship are deeply interconnected. However, there are tensions between the recognition of and interest in weaving Indigenous knowledge into environmental stewardship, while adhering to Indigenous knowledge governance principles that ensure protection and prevent extraction, exploitation, or misuse. Growing from this tension, this study is situated in a collaborative Marine Spatial Planning (MSP) program on the South Coast of British Columbia where federal, provincial, and First Nations governments are partnering to envision and plan marine use in the region. Using community-based participatory research methodologies, this study was developed with First Nations partners at the First Nations Fisheries Council of British Columbia (FNFC) and asks how Indigenous knowledges may be ethically and equitably woven into the marine planning process. To do this, I hosted focus groups and interviews with individuals working for each of the MSP partners and sought to better understand perspectives on and experiences with knowledge governance in collaborative environmental stewardship work. The intention driving this study was to provide insight and potential recommendations that may support the FNFC and partners in establishing an MSP process that adhered to and was founded in Indigenous knowledge governance principles and practices. Project findings demonstrate that, rather than understanding knowledge as an object or evidence base separate from people and governance, knowledge systems must be recognized. Thus, expanding mainstream conceptualizations of knowledge governance to include support for and recognition of the systems and people that generate, practice, and hold knowledge. From this vantage, operational considerations include both technical approaches and tools, as well as transformational change required at a societal and individual level. This transformational change must be situated in decolonizing theory and grounded in everyday realities and practices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.267
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0190.065
Scholarly communication0.0160.008
Open science0.0020.011
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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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