Tapping into agglomeration benefits by engaging in a community of practice
Bibliographic record
Abstract
While a great deal is known in the agglomeration literature regarding the importance of having access to Marshallian externalities for firm performance, less is known about how geographically isolated and remote firms fare with the lack of such access. More recent literature suggests that firms, especially those within geographic proximity, can form a community of practice to facilitate deliberate learning and collectively create a shared repertoire, that is, a set of communal knowledge of procedures, techniques, and standards for best practices. Unlike Marshallian externalities, however, community of practice membership is not necessarily bounded by geography, and as such, isolated firms can also engage in a community of practice and unlock the shared repertoire for their own benefits. The study of the Ontario wine industry (1999–2009) finds that community of practice engagement weakens the detrimental impact of geographic isolation on firm performance, suggesting that isolated firms can tap into agglomeration benefits by engaging in a community of practice.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".