Forty years of modelling rapid transit’s land value uplift in North America: moving beyond the tip of the iceberg
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying and measuring the land value uplift (LVU) impacts of rapid transit are important for a number of reasons. However, despite the general notion that rapid transit does confer positive LVU benefits, our comprehensive and critical review of more than 130 analyses across 60 studies completed in North America over the past 40 years finds significant heterogeneity in research outcomes, leaving many significant questions unanswered. Beyond high-level differences in study inputs, we argue that a fundamental source of variability is a lack of empirical specificity from the use of proximity as the dominant way in which LVU benefits are captured. This use of a proxy leads to the potential for omitted variables and unobserved relationships, and exposes previous work to the potential for misvalued results. To overcome this issue, we outline recommendations for future research, namely a recognition of relative accessibility and the possibility of LVU impacts from transit-oriented development. Incorporating measures related to these factors into LVU models can reveal their implicit prices, resulting in research that is more theoretically inclusive, empirically comprehensive, comparable, and able to provide important information to inform policy analysis and prescription.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it