Indigenous Economies for Post-Covid Development
Bibliographic record
Abstract
Despite being disproportionately susceptible to infectious diseases like COVID-19, many Indigenous peoples still hold traditional knowledge that is responding and adapting to new circumstances and crises such as the pandemic. In this paper, we present the findings from a participatory video project in eight Makushi and Wapishan Indigenous communities in the North Rupununi, Guyana, that explored the difficulties and disruptions that came about through COVID-19, but also the opportunities for change and transformation. Over four months, Indigenous researchers gathered the views and perspectives of their communities through a participatory video process. Our findings show that there was limited information provided to communities and their leaders (especially at the start of the pandemic), and support, in the form of supplies and relief, was ad-hoc and inconsistent. As people lost income from paid work, they turned to traditional farming, fishing and hunting to sustain their lives and to support others who did not have the conditions to support themselves. While many Indigenous community members retreated to their isolated farms as a protective measure, community leaders took responsibility to protect their lands and territory by installing gates on access roads and establishing patrols to enforce rules. The recognition that their traditional knowledge was not only culturally important but necessary for survival during the pandemic, gave it a newfound relevance and legitimacy, particularly for young people. Supporting Indigenous economies such as farming are not only critical for maintaining nature and traditional cultures today, but also for being resilient to future social and ecological crises.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".