THE FORCES OF CODIFICATION: KNOWLEDGE, SUPPLY CHAIN RESTRUCTURING AND INNOVATION IN THE WINDSOR ONTARIO MACHINE TOOL AND MOULD CLUSTER
Bibliographic record
Abstract
There is a growing recognition that despite the difficulties confronting firms in formalizing knowledge, the contemporary economy is characterized by intensifying pressures for the conversion of tacit into codified, if not commodified knowledge. Thus researchers are increasingly recognizing that globalization is associated with strong pressures for the codification of often highly localized tacit knowledge through the construction of ‘global pipelines’ to tap into ‘local buzz’. In this paper we examine the impact of supply chain restructuring in the auto industry, the adoption of new technology and state innovation policy on innovation and knowledge in the Windsor, Ontario machine tool die and mould (MTDM) cluster. While much research emphasizes the need for trust and reciprocity in knowledge exchange we argue that the codification of knowledge is occurring in a context of significant asymmetries in power relations between Original Equipment Manufacturers (OEMs) and suppliers, in which overcapacity and price and profit pressure determine the nature of knowledge transfer.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".