Bibliographic record
Abstract
While the literature on place effects often emphasizes the distinction between people-based and place-based attributes, the present research explores daily interactions between people and places. Through the study of perceived neighborhoods and activity spaces, I first explore people’s place experiences and discuss approximations that emerge in the interpretation of place effects when places are operationalized as uniform areas that neglect people’s socially differentiated experiences of cities and urban resources. Focusing on the daycourse of places, I then underline how places may change hourly, especially with regard to the social composition of the population and the density of services available. This allows me to explicitly incorporate a temporal dimension to places and their effects.This double focus on the daily dynamics of people and places is at the heart of my research about place effects and social inequalities that may arise from them. Through quantitative work carried out in the cities of Paris and Montreal, I aim to highlight how research on social inequalities – just as area-based interventions that are developed to reduce them - would benefit from considering the daycourse of place effects.
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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 | 0.008 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".