Broadening ecological footprint and biocapacity research: A co-developed research agenda with Canadian stakeholders
Bibliographic record
Abstract
The Ecological Footprint and Biocapacity methodology and data set (EFB) are a rigorous and longstanding method for measuring sustainability through trade and consumption worldwide. It goes through regular methodological advancements and is used by countries and researchers worldwide. However, the uptake of the approach is lacking across Canadian cities and sustainability groups. This study assessed the understanding, and perceptions of EFB among sustainability stakeholders in Canada to identify barriers and opportunities for increased uptake. We conducted 23 interviews with stakeholders from non-governmental and governmental organizations across western, central, and eastern Canada. The data was analyzed through an affinity sort and revealed themes which resulted in a broader research agenda focusing on social science questions centered around EFB. The identified areas for future research include source data, complexity and scale, behaviour, and policy. The resulting research agenda informed by stakeholders aims to enhance and broaden the use of EFB. The research agenda brings EFB into new areas of inquiry relevant to diverse sectors while also fostering multidisciplinary approaches. Advancing EFB methodologies and applications will enable researchers to contribute more significantly to global sustainability efforts. • The paper presents a co-developed research agenda, formed in collaboration with Canadian sustainability stakeholders, aimed at advancing the integration of Ecological Footprint and Biocapacity (EFB) methodologies into Canadian sustainability policy. • Stakeholders identified four main thematic areas for research on EFB to make it more useful for them. • Several key research questions are outlined that focus on improving data transparency, enhancing EFB's adaptability across different scales, incorporating cultural indicators, and using EFB to support reconciliation and regional sustainability efforts.
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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 | 0.160 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.046 | 0.022 |
| Scholarly communication | 0.030 | 0.019 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".