The Location of High Knowledge Content Activities in the Canadian Urban System, 1971-1991
Bibliographic record
Abstract
It is widely recognized that certain activities have a higher capacity than others topromote economic growth and development. Many of these more dynamic activities areoften said to involve “high technology”. In this paper we first question the conceptualand operational utility of the notion “high technology”. We then propose a morestraightforward and more easily measured concept —high knowledge content—,demonstrating that activities of this nature may be found in “low tech” sectors. By means of an empirical analysis, we then attempt to contribute to a better understanding of the locational dynamics of high knowledge content activities within the Canadian urban system over the period 1971-1991. Specifically, we seek to determine if this class of activities is becoming spatially more concentrated or more dispersed across the urban system. The answer to this question is particularly important for smaller communities in peripheral regions whose economic bases are highly dependent upon “low tech” activities.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".