Pop-up restoration in colonial contexts: applying an indigenous food systems lens to ecological restoration
Bibliographic record
Abstract
As environmental injustices and their disproportionate harms to Indigenous communities are increasingly acknowledged, restoration strategies are being deployed widely by environmental NGOs, resource extraction industries, and government agencies. The inclusion of Indigenous communities and their knowledges in restoration efforts are often considered progress in the pursuit of ecological reconciliation. However, in some cases we have observed a lack of meaningful progress as settler colonial prescriptions for land-healing can eschew efforts to decolonize ecological restoration — what we have labeled “pop-up restoration.” We consider two restoration efforts underway in St’at’imc and Quw’utsun territories (Canada) and contrast them with what we are learning alongside the communities’ own values and efforts to reclaim and revitalize food systems throughout forest, wetland, and grassland systems. Utilizing culturally appropriate pathways, we then evaluate how applying an Indigenous Food Systems lens to ecological restoration may provide a framework to remedy pop-up restoration, confronting settler colonial aspirations to transform Indigenous homelands while asserting justice in ecological restoration contexts.
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
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.077 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".