Goal-Driven or Data-Driven? Inventory of Sustainability Indicator Initiatives in Rural Canada
Bibliographic record
Abstract
This article seeks to address knowledge gaps on sustainability indicators (SIs) in rural and natural resource-dependent communities, considering how they are used to contextualize sustainable development priorities and support local governance. We build on recent scholarship on the potentials of SIs for stimulating societal transformation, extending this inquiry into rural and resource-based communities which have been under-represented in SI research. The governance challenges facing rural Canada, as well as its geographic and socio-economic diversity, provide a unique context for examining these issues. We provide relatively uncommon synthetic findings by compiling an inventory of SI initiatives across 39 rural communities and regions of Canada. Using the Community Capital Framework, we examine grey literature and academic publications related to each initiative spanning from 1999–2019 to determine the breadth of sustainable development priorities considered. Informed by collaborative and multi-level governance frameworks, we explore how these initiatives are used to support multi-stakeholder collective action. This article finds that rural Canadian SI initiatives prioritize socio-cultural capital, with relatively fewer economic and ecological indicators, while identifying a typology of SI use and inter-related governance dynamics informing how these priorities and indicators are determined. Although some initiatives display highly collaborative and bottom-up processes, many rural Canadian SI initiatives are characterized by a data-driven approach that, when met with local capacity gaps, fails to contextualize standardized datasets to reflect rural realities. We encourage more in-depth investigation of these findings and comparison of Canadian experiences to other jurisdictions.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.035 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 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".