Innovation practices within small to medium-sized mechanically-based manufacturers
Bibliographic record
Abstract
Manufacturing SMEs remain an underdeveloped area of interest in the literature on innovation. SMEs whose principal skill sets are mechanical in nature (MechSMEs) and that serve multiple customer groups offer a particularly rich context for the study of innovation practices that are mostly under the control of the firm’s managers. This empirically-based paper uses case studies based on multiple units of analysis within each firm so that the overall innovation practices of four mature firms (average firm age 45 years, minimum 20 years) and the practices used within thirteen specif ic innovations (both product and process innovations) were studied. This combination of studying firm-specific and innovation-specific practices was used to construct a picture of the most important innovation practices within each firm.Based on the identification of the two most innovative firms, the findings indicate that approximately half of these firms’ innovation practices were shared with the other firms, while the other half of the practices were found to be either idiosyncratic or only partially shared. Of particular interest were fifteen innovation practices that were particularly influential within the two most innovative firms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".