Individual and Collective Learning in Community-based Planning Groups in Mimico, Canada
Bibliographic record
Abstract
This qualitative study examines individual and collective learning amongst participants of three community-based planning groups in Mimico, part of the City of Toronto, Canada: the Ward 6 Community Action Team (Ward 6 CAT), the Lakeshore Planning Council (LPC), and the Mimico Lakeshore Network (MLN). Twenty-three interviews conducted with participants of these groups, analyzed and coded, informed my selection of data and focus on the key themes explored. Two major questions guided the research: (a) How are learning and human development implicated in community-based networks and coalitions, individually and collectively, in and through people’s participation? And (b) What forms of learning are particular to the process of coalition and network work as people engage with one another and one another’s groups, and learn about their commonalities and differences and engage in collaboration, negotiation, and (sometimes) dissipation and disassociation? This study contributes to the small but growing field of social movement learning, whose scholars have applied cultural-historical activity theory (CHAT) to an analysis of informal learning amongst individuals active in community organizations. Key concepts within the CHAT framework, such as artifact mediation, contradiction, and activity systems, are explored. Two interviewees and myself as participant observer informed my analysis of the internal dynamics of MLN, which are examined through its relationship with Ward 6 CAT and through LPC’s departure from the network. Members of MLN engaged in a form of collective learning as they continued to work together as a group unto themselves. They remained united despite LPC’s departure from the network. Detailed accounts of two other interviewees provide a deeper look into the social dynamics of learning for individuals within collective activity systems and the collective activity systems themselves. Both interviewees provide excellent examples of community activists who engaged in individual and collective learning as part of their participation in their respective community-based groups. Their learning experiences were connected to their experience and expression of emotion, as well as contradictions within the activity system of the City of Toronto and in the activity systems of the community-based groups they worked with.
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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 | 0.002 | 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 | 0.024 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".