Equity planning and community economic development : the case of Cote des Neiges-Notre Dame de Grace
Bibliographic record
Abstract
The developed world has witnessed high levels of economic growth, however, the benefits of this growth have not been felt evenly across communities and nations. Moreover, despite the efforts of locally based community organisations, marginalised communities remain unable to access many mainstream social and economic resources. This thesis links the Equity Planning Model with Community Economic Development (CED) as a way for urban planners and CED practitioners to better meet the needs of marginalised groups and communities. This thesis provides an analysis of the district that identifies marginalised communities according to social and economic factors, within the district of Côte des Neiges-Notre Dame de Grâce in Montreal, and provides an indication of the residents socio-economic needs and goals. From this, a framework for an economic development plan is articulated that uses small businesses for community development. This framework (1) identifies the marginal communities in the district, (2) identifies three development goals, and (3) provides recommendations, based on these goals, for the three small business start-up organisations in the district. The methods used in this thesis demonstrate how, by specifically identifying the needs of marginalised groups, economic development plans can ensure that such groups gain access to social and economic opportunities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".