Greening the Inner city: Eco-Friendly Community Development
Bibliographic record
Abstract
The second WIRA Summer Institute - Greening the Inner City: Eco-friendly Community Development – was held from June 2nd to 7th 2003. \n \nThe WIRA Summer Institute is designed with a number of objectives in mind. The intention of each Summer Institute is to create a unique, shared learning experience for university students and community practitioners through a series of workshop-style sessions addressing a range of key issues related to community development in Winnipeg’s inner city. The Summer Institute aims to take a “hands-on” approach to learning, to combine classroom learning with “in the field” experience of Winnipeg’s inner-city communities, and to provide instruction that combines theory with practice. \n \nThe 2003 Summer Institute "Greening the Inner city: Eco-Friendly Community Developments" course is intended for community workers, residents and university students, and will explore issues of environmental sustainability in the inner city. By drawing extensively on case studies and field project work, this course will examine challenges and successes of environmentally-sensitive community development. Topics will include: strategies for ‘greening’ the neighbourhood, energy-efficient housing development, and the role of transportation. A focus throughout will be the potential to create jobs and build skills for community residents using environmentally-friendly community development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".