Attempting to Address Conditions of Poverty through an Inclusive Economic Approach in Alberta
Bibliographic record
Abstract
This article responds to the call to alter current economic and social systems in light of COVID-19 by documenting initial efforts by a community-university partnership to build an inclusive economy. First, the effects of neoliberalism and oil ex- traction are examined to highlight the inequality that existed in Alberta long before the pandemic began. The paper then outlines four key areas of change: employment and training; social procurement and community benefits agreements; living wages; and basic income. It concludes with some initial learnings that may have resonance for others attempting to stimulate economic practices that distribute wealth more equitably. RÉSUMÉ Cet article répond à un désir de modifier les systèmes économiques et sociaux actuels en conséquence du COVID-19. Il le fait en décrivant des efforts préliminaires de la part d’un partenariat communauté/université pour établir une économie qui soit plus inclusive. L’article examine d’abord les conséquences du néolibéralisme et de l’extraction du pétrole afin de souligner les inégalités qui existaient déjà en Alberta bien avant le début de la pandémie. L’article présente ensuite quatre domaines clés qu’on aurait besoin de modifier : l’emploi et la formation; les ententes sur l’approvisionnement social et sur les avantages communautaires; le salaire de subsistance; et le revenu de base. L’article conclut en faisant des ob- servations préliminaires qui pourrait inspirer d’autres individus tentant d’encourager des pratiques économiques orientées vers une distribution plus équitable de la richesse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".