Firms’ linkages with universities and public research institutes in Argentina: factors driving the selection of different channels
Bibliographic record
Abstract
Knowledge flows between public research organisations (PROs) and firms may occur through various channels. Channel selection may have different drivers and effects. Although much research has been carried out on the drivers of firms and researchers to connect with each other, less attention has been paid to the determinants of the selection of different channels of interaction. This research analysis factors driving firms’ selection of different channels of interactions with public research organisations (PROs), both public research institutes (PRIs) and universities (UNIs). The paper estimates bi-variate probit models with sample selection using micro data for 2007 from a representative survey of Argentinean firms. The classification of channels is based on previous research for Latin America and includes four types according to the main goals that firms and public research organisations seek when interacting: traditional, service, commercial and bi-directional channels. We find that factors driving the selection of the bi-directional channel are different from those driving selection of the others. In particular, firms choosing this channel employ a more skilled workforce and generally interact with PRIs and UNIs in order to benefit their own innovative activities. Thus, this commitment to knowledge capabilities and innovation when firms use the bi-directional channel may enhance the potential of PRO–firm interactions to upgrade the national innovation system (NIS).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".