Making the Road: Community Benefits Organizing in Canada as a Radical Adult Learning Practice
Bibliographic record
Abstract
Canadian organizing efforts aiming to democratize the economy are the focus of this study, specifically if and how emancipatory learning is occurring in Community Benefits Agreements (CBA) campaigns and coalitions. It further aims to delineate CBA organizing as a community power-building strategy in the interests of workers and communities. Turning to dialectical frameworks, Cultural-Historical-Activity-Theory (CHAT) enables consideration of organizing as radical adult learning in a Freirian tradition, while critical place inquiry provides a methodological approach that is concerned with the socio-materiality of human practices as shaped by and in place. History and memory methods centre the voice, agency and perspective of the persistently marginalized communities impacted by CBA organizing. Drawing on secondary sources, coupled with my own experience embedded in this movement activity, I provide case studies of five neighbourhood-based CBA campaigns in two Canadian cities: Toronto and Ottawa. The cases show these campaigns build on previous local leadership and community capacity development, while concurrently equipping constituencies for collective deliberation and action. CHAT further enables an activity analysis of CBA organizing environments that suggests an expansive view of place is foundational for community power-building. It further identifies that collective memory, stories and narrative – coupled with an ethical orientation to movement building – hold promise as practices that could enable and amplify the emancipatory potential of organizing. Finally, Indigenous ontologies and axiology are proposed as a potentially profound source of guidance to Canadian organizing efforts challenging oppressive systems and structures while building collective community agency and power from the ground up.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".