Bibliographic record
Abstract
The creation of the Secondary Plan for the Golden Mile neighbourhood in Scarborough, Ontario and the construction of the Eglinton Light Rail Transit (LRT) system have sparked the potential for further development and growth. To leverage the anticipated changes being proposed in their neighbourhood, community members within the Golden Mile should enter into a Community Benefit Agreements with the respective developers to secure a wide variety of resources that may be shared by all. Building uponthe existingCommunityBenefitsFrameworkthat hasbeencreated by local non-profit organizations, this Major Research Paper (MRP) seeks to assist residents so that they may achieve their targets and goal. To support these efforts, a list of possible benefits that could be negotiated within a CBA was identified within three broad categories: addressing the need for affordable housing; building sustainable communities; and creating socio-economic opportunities for residents. Transforming recommendations into actual benefits can only be achieved by promoting collaborative efforts with other stakeholders, by mutual understanding and learning of each stakeholder’s respective position, and by creating value so that everyone involved may gain from the provisions of such benefits.
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 | 0.027 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.052 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".