Activity Spaces and the Measurement of Clustering and Exposure: A Case Study of Linguistic Groups in Montreal
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
Population segregation measurement is a topic of broad interest in the social sciences. In this paper we draw from recent advances in the spatial analysis literature to derive individualized measures of clustering and exposure. Recent research on accessibility has seen a shift from place-based measures to person-based ones. Similarly, the notion of residential clustering and exposure patterns, while typically related to the distribution of population in zonal systems, can be modified to account for heterogeneous experiences of urban space. In particular, at the individual level, the degree of clustering and exposure is related to personal mobility and the individual experience of space. In this paper we turn to the question of whether individuals belonging to different groups and living in different areas of a city observe differences in their clustering and exposure to population groups over space. The proposed procedure is applied empirically to the case of Montreal to explore how native English speakers of various levels of mobility experience exposure.
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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 | 0.000 | 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