A residential location search model based on the reasons for moving out
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
Modeling spatial search processes such as residential location search are challenging, particularly, due to the need to deal with a large dataset and wide array of factors. This introduces a multi-dimensionality challenge to location search modeling. With the motivation to accommodate multi-dimensionality, this paper develops a machine learning–based Gaussian mixture model (GMM) for location search. This study accommodates the effects of several factors including accessibility, land use, dwelling, transportation infrastructure, and neighborhood attributes on location search decisions. The spatial unit of analysis is dwelling-level. This study conceptualizes that households’ search for location based on their reason to move. The pool of alternatives for each household is generated based on probability estimates of GMM. The location choice model considering the reason-based GMM outperforms the model without considering relocation reasons in GMM and random sampling-based model in-terms of predictive performance. The search model has been implemented in an integrated urban model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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