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Record W3178266678 · doi:10.1007/s11625-021-00996-x

Learning from knowledge co-production research and practice in the twenty-first century: global lessons and what they mean for collaborative research in Nunatsiavut

2021· article· en· W3178266678 on OpenAlexafffundabout
Melanie Zurba, Michael A. Petriello, Carly Madge, Paul McCarney, Breanna Bishop, Samantha McBeth, Mary Denniston, Hekia Bodwitch, Megan Bailey

Bibliographic record

VenueSustainability Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsDalhousie University
FundersDivision of Arctic SciencesCanada First Research Excellence FundOcean Frontier InstituteDalhousie University
KeywordsKnowledge managementContext (archaeology)SustainabilitySociologyComputer scienceEcologyGeography

Abstract

fetched live from OpenAlex

Abstract An increasing need for novel approaches to knowledge co-production that effectively and equitably address sustainability challenges has arisen in the twenty-first century. Calls for more representative and contextual co-production strategies have come from Indigenous communities, scientific research forums, and global environmental governance networks. Despite calls to action, there are no systematic reviews that derive lessons from knowledge co-production scholarship to interpret their significance through the lens of a specific sociopolitical and cultural context. We conducted a systematic review of peer-reviewed and grey literature on knowledge co-production published from 2000 to 2020. Using a hybrid inductive and deductive thematic analysis, we identified two conceptual themes—guiding principles and approaches—to structure the synthesis and interpretation of 102 studies. We found that knowledge co-production studies often converged on four interrelated principles: recognition of contextual diversity bounding knowledge co-production, preemptive and intentional engagement with Indigenous knowledge holders, formation of shared understanding of the purpose of knowledge co-production, and empowerment of knowledge holders throughout the co-production cycle. These principles manifested in multiple approaches for interpreting, bridging, applying, and distributing power amongst diverse knowledge systems rooted in different epistemologies. We filter these findings through the social–ecological context that frames an ongoing knowledge co-production project with Inuit communities in Nunatsiavut, Canada: theSustainable Nunatsiavut Futures Project. Our review suggests that emerging forms of knowledge co-production principles and approaches yield immense potential in diverse contexts. Yet in many regions, including Nunatsiavut, principles alone may not be enough to account for systemic and contextualized issues (e.g., colonisation and data sovereignty) that can present roadblocks to equitable sustainability science in the twenty-first century if left unaddressed.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
gptMetaresearchScience and technology studies
Domain: Methods · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.044
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.978
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0220.073
Scholarly communication0.0290.022
Open science0.0040.018
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.182
GPT teacher head0.548
Teacher spread0.366 · 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

Labeled directly by 2 models reading the full record.

Science and technology studiesMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainMethods
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

Citations134
Published2021
Admission routes3
Has abstractyes

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