Bibliographic record
Abstract
The built environment of a city is the result of cumulative land-use decisions and investments. Most of the research on Land Value Capture (LVC) is focused on site-specific mitigation. This study proposes a five Es approach to analyse LVC-supported climate change financing through engineering, education, encouragement, enforcement, and evaluation. This approach aligns with Canada's 2030 Agenda National Strategy and climate action plans. The framework is tested on a pilot case - the City of Ottawa. The assessment shows Ottawa needs to prioritise enforcement and evaluation aspects of climate financing. This study recommends a special budgetary provision to support climate-friendly projects municipally, linking existing and proposed climate projects and program funding to LVC sources of funds provincially and encouraging funding capacity building to create climate change awareness federally. A national policy document, evaluation framework and urban reform agenda are recommended for an enabling policy environment for LVC to support climate projects.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| 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".