The identification of employment centres in Canadian metropolitan areas: the example of Montreal, 1996
Bibliographic record
Abstract
The intrametropolitan distribution of economic activities and, specifically, the formation of suburban employment centres has become a major research and policy issue. In spite of an increasing number of detailed analyses of the geography of employment in individual metropolitan areas, no generally accepted and systematic methodology for identifying employment centres exists. Comparisons between metropolitan areas have been highly limited due to both a lack of consistent and comparable data and a plethora of methods. We first present an overview of various methods that have been used to identify employment centres. Using Montreal as a case study, we then evaluate the suitability of various methods in the light of available data on job location in Canadian metropolitan areas. The method that yields the best results is one based upon dual criteria applied at the census tract level: a total employment threshold and the ratio of employment to the resident workers. We use this method to identify the form of the Montreal space‐economy in 1996. The identification of a suitable, although imperfect, method represents a first step towards being able to more objectively and systematically examine a wide range of issues concerning metropolitan economic structure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".