Regenerating Place: Highlighting the Role of Ecological Knowledge
Bibliographic record
Abstract
As many local places globally suffer from ecological and social decline, sustainability research increasingly recognizes the critical importance of studying organizational efforts toward regenerating local communities and ecosystems. This emerging research, however, overlooks the role of ecological knowledge, that is, place-based understanding of the processes and functions of the ecosystems in which organizations operate. As such, we ask “How do organizations harness ecological knowledge to advance the regeneration of local places?” Through an inductive study of nine certified organic farming organizations on Vancouver Island, Canada, we find that organizations engage in three cyclical and closely interlinked practices of identifying, acquiring, and applying ecological knowledge which together enhance their organizational performance while contributing to regenerating the local social-ecological systems. Our empirically grounded model of leveraging ecological knowledge contributes to research on sustainability and place, and to studies of regeneration, by uncovering the specific practices that enable firms to develop place-based regenerative solutions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".