Innovation in the IT sector: intermediary organisations as a knowledge sharing strategy?
Bibliographic record
Abstract
While traditional neoclassical economics view the individual entrepreneur as source of competitiveness, and the market economy as source of economic growth, it has recently been recognised that collective leadership and entrepreneurial activity are often at least as important. Also, knowledge has replaced physical capital as the main source of competitiveness and creation of a competitive advantage over other firms through networks and knowledge sharing. However, it is often unclear how this knowledge can be gained. Some authors contest the neoclassical individualistic view and consider that networks and industrial clusters can foster such knowledge exchanges. We hypothesised that the IT sector would be interested in collective ways of accessing knowledge, and sought to determine what modes of governance or intermediaries could make this happen. The paper analyses these intermediaries and their contributions to the IT/multimedia/gaming sector in Montreal, the cluster policy which favours knowledge exchanges, the collective over the individual.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".