The external benefits of government subsidized rehabilitation programs
Bibliographic record
Abstract
This thesis examines the hypothesis that government housing policies - in particular rehabilitation policies - create positive spillover effects (externalities) to surrounding homes, buildings, and property values in general. These effects are examined for two reasons: (a)There is a large body of literature focussing on externalities created by government housing policies, but very little of this theory has been tested empirically; (b) The recent implementation of rehabilitation policies have been based in part on the expectation that the policies create positive neighborhood effects. In this study, an empirical analysis is performed on one rehabilitation program — the Canadian Residential Rehabilitation Assistance Program (RRAP). The program is analyzed via multivariate regression analysis to determine if any externalities are created by this housing policy. The determination of externalities is important in understanding the factors affecting neighborhood change. This study investigates the relationship between externalities and expectations and discusses how these factors can have large impacts on reinvestment patterns which create positive neighborhood change. The empirical findings of this study indicate that there are no externalities created by the RRAP program. This research suggests that such programs be implemented on the basis of the individual benefits for the recipient. The findings imply that similar rehabilitation programs will not create positive externalities or expectations and, hence, cannot be factors affecting neighborhood change. As a result, policy analysts should conclude that programs similar to RRAP should not be implemented solely on the basis that there are indirect external benefits to the neighborhood.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".