Bibliographic record
Abstract
This paper develops a dynamic geographic concentration model by analyzing the founding rate and the death rate of firms within and outside the industrial clusters through an industry’s life cycle. Data from the Ontario’s wine industry (1865 – 2007) provides preliminary evidence. Social scientists have long noticed that firms engaged in the same business tend to be co-located with each other in a small number of separate places. This phenomenon was labeled as “economies of agglomeration ” by Alfred Marshall (1920) in his neo-classical Principles of Economics. More recently, the term “industrial cluster ” – defined as geographic concentrations of interconnected companies and institutions in a particular field – was coined (Porter, 1998). Whatever it is labeled13, the phenomenon of geographic concentration has attracted much attention from different social science disciplines over the past centuries. From the economic perspective, firms co-locating with each other obtain economic gains by sharing exclusive input factors such as transportation convenience (Von Thunen, 1826), natural resources (Hoover, 1948, 1968), skilled labors (Weber, 1909), specialized suppliers, knowledge spillover etc. (Marshall, 1920; Krugman, 1991b). This perspective generally posits that clustered firms outperform their non-clustered counterparts due to exclusive economic gains from co-location. Thus, geographic
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.252 | 0.146 |
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".