The Impact of the COVID-19 Pandemic on Aboriginal Economic Development Corporations (AEDCs), 2019–2021: An Interview-based Perspective with CEOs
Bibliographic record
Abstract
Collaborating with the Canadian Council on Aboriginal Business (CCAB), the authors investigate how Aboriginal Economic Development Corporations (AEDCs) responded to and, in most cases, weathered the commercial disruptions associated with the COVID-19 pandemic. Working from survey interviews, supplemented by business data from previous CCAB national surveys and other governmental information, the authors explore the challenges that CEOs faced and how they managed their companies through the COVID crisis. For many of the AEDCs respondents, the problems they were facing were not necessarily brought on by the pandemic but were outgrowths of pre-existing socioeconomic disparities that had been exacerbated by COVID-19. Throughout the pandemic, these CEOs battled to maintain operations, manage and support staff in trying circumstances, and keep their assets operating or safely managed. They frequently assisted their home communities with services not normally within their purview, including producing PPE products and delivering groceries and medicine to remote communities. This report focuses on crisis management and can be a useful reference point for policymakers and decision-makers looking to create coherent responses to whatever the next crisis faced by EDCs might be.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".