THE INNOVATION PROCESS IN LOCAL DEVELOPMENT – THE MATERIAL, INSTITUTIONAL, AND INTELLECTUAL INFRASTRUCTURE SHAPING AND SHAPED BY INNOVATION
Bibliographic record
Abstract
The purpose of the article is to define the material, institutional, and intellectual infrastructure of a region and identify the innovative processes that determine its creation. Our main research hypothesis is that the processes that influence the creation of a region’s infrastructure determine a region’s competitiveness as well. To verify these premises, we conducted a study among the residents and employees of a municipality. The research employed deductive and inductive methods and a qualitative analysis was performed. Pearson’s linear correlation coefficient and factor analysis (inference based on the modal and median values) were used in the study. The research verified the hypothesis that innovative processes influence the creation of a region’s infrastructure and that innovative processes in the studied region exhibit low dynamics, which is caused by financial and psychosocial barriers. The important role of social leaders in municipalities was identified as well, above all as regards building civic society and social activity. The added value of the article is threefold: the developed model of infrastructure construction in the material, institutional, and intellectual dimensions of a region; recommendations for the investigated municipality; and a structured questionnaire that, together with the model, can be used for research in municipalities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".