The ‘constant size neighbourhood trap’ in accessibility and health studies
Bibliographic record
Abstract
In literature on neighbourhood effects and resources accessibility, the number of neighbourhood resources to which residents may have access are often estimated from spatial units whose constant size fails to account for unique ways residents experience their neighbourhoods. To investigate this ‘constant size neighbourhood trap’, we compared numbers of healthcare resources included in Constant Size Buffers (CSBs) and in Perceived Neighbourhood Polygons (PNPs) from cognitive neighbourhood data collected among 653 residents of the Paris metropolitan area. We observed that residents of deprived and peripheral areas had smaller PNPs than their counterparts. Studying residents assessments of the quantity of neighbourhood practitioners, we then assessed the validity of using PNPs rather than CSBs to estimate number of neighbourhood resources. Lastly, resource inequalities across the Paris metropolitan area were found to be far wider when considering PNPs rather than CSBs. Using constant neighbourhood delineation can lead to inaccurately measured individual accessibility to neighbourhood resources and to downplay the extent of inequalities in urban resources.
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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 | 0.093 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".