Combined innovation and export strategies of KIBS in different regional settings
Bibliographic record
Abstract
This paper examines how KIBS establishments combine innovation and exports, and which factors are associated with these combinations. In particular, we hypothesize that KIBS establishments which both export and innovate will be over-represented in metropolitan regions, and under-represented in peripheral regions. Our analysis draws upon a sample of 429 innovative KIBS establishments in the province of Quebec (Canada). We show that strategies differ across space (but not as expected – metropolitan and peripheral strategies are similar), that T-KIBS’ strategies are associated with non-market information sources, and that P-KIBS' strategies are associated with information purchasing. P-KIBS’ and T-KIBS’ strategies vary with the performance of in-house R&D. Taken together, these results suggest that whereas KIBS’ choice of export and innovation strategies do not reflect their ‘hard’ or ‘soft’ nature, the factors associated with this choice do. The similarity between metropolitan and peripheral regions reflects the fact that Quebec’s resource-based peripheral economy is international and innovative.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".