Mobilizing transdisciplinary sustainability science in place-based communities: Evaluating saliency, legitimacy, and credibility in northern Canada
Bibliographic record
Abstract
The field of transdisciplinary sustainability science offers limited guidance on what it means to mobilize knowledge outside of conventional policy and decision-making settings. Research within this field tends to emphasize knowledge mobilization for conventional environmental policy venues and decision-makers such as state and industry actors. Place-based communities often make critical management decisions to advance sustainability and inform policy, yet the evaluation of sustainability science in these contexts is underexamined. Using a case study, community-based research approach, we explored how social processes in place-based communities shaped interpretations of sustainability science by those involved in and/or affected by research. We used core criteria for knowledge mobilization—salience, legitimacy, and credibility, as established by Cash et al. (2003) — to guide our analysis of how research knowledge was evaluated. Our analysis highlighted that specific relationships, perspectives and worldviews, and historical contexts shaped how salience, legitimacy and credibility were interpreted. We affirm that for knowledge to be effectively mobilized, it must be salient, legitimate and credible, but find that the definitions of these terms are highly dependant on the social contexts in which the research takes place. These insights are critical to future transdisciplinary research aimed at addressing complex sustainability problems impacting place-based communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".