THE EMERGENCE OF INNOVATION-BASED WIRELESS CLUSTERS: QUALITY AND TIMING MATTER
Bibliographic record
Abstract
This study compares the emergence of four wireless clusters in the 1970s and 1980s. Two of them, Calgary in Canada and Finland, initially pursued rather similar service innovations for not very different markets but with very different outcomes, which raises the question why. One major reason that emerges from the reviewed extant research on cluster emergence and innovation diffusion concerns the differences in timing and quality of the initial innovations, affecting their respective perceived diffusion attributes, and market growth and extent. The initial innovation in Finland was well received, diffused rapidly and eventually globally, and led to a positive spiral spurring the industry on to take a global lead. In the case of Calgary, however, it was un-competitive in the broader international market, forcing the anchor firm to adapt and reorient. The study analyses and compares the characteristics of the respective initial innovations and their impact on the outcome, and concludes with a discussion and some propositions on cluster emergence. Enhanced understanding of nascent clusters, especially regarding the role of globally attractive initial innovations and their diffusion quality and timing, should provide value for both scholars and practitioners.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".