Is Distance Really Dead? Comparing Industrial Location Patterns over Time in Canada
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
This article presents a model for comparing industrial location patterns over time, applied to Canadian data for 1971 and 1996. The Canadian economy is divided into eighteen industrial sectors (manufacturing and services), of which eight are examined in detail. The analysis addresses several questions. Do observed location models for given industries follow predictable patterns? How stable are those patterns over time? Has the relative sensitivity to “distance” of given industries changed over time? Can significant breaks in location patterns be observed over time? The authors consider the possible impact of information technology on location. If “distance is dying,” as is sometimes argued, this should be reflected in changing location patterns. The results show a high degree of stability over time, suggesting that distance is still very much alive.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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