Conceptualizing and operationalizing neighbourhoods : the conundrum of identifiying territorial units.
Bibliographic record
Abstract
Background: Over the past 10 years, there has been a surge of interest in studying smallarea \ncharacteristics as determinants of population and individual health. Accumulating \nevidence indicates the existence of variations in the health status of populations living in \nareas that differ in affluence and shows that selected small-area characteristics are \nassociated with the occurrence of selected health behaviours. These variations cannot be \nattributed solely to differential characteristics of populations living within small areas. One \nvexing problem that confronts researchers is that of conceptualizing and operationalizing \nneighbourhoods through delineation of small territorial units in health research. \nGoals and Methods: The aims of this paper are to selectively overview conceptual \ndefinitions of neighbourhoods and to illustrate the challenges of operationalizing \nneighbourhoods in urban areas by describing our attempts to map out small territorial \nunits on the Island of Montreal and in the City of Calgary. \nConclusion: We outline guiding principles for the construction of a methodology for \nestablishing small-area contours in urban areas and formulate recommendations for future \nresearch.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".