Actividades intensivas en conocimiento en Costa Rica: una aproximación para medir la base de conocimiento de la economía
Bibliographic record
Abstract
Using clustering techniques, an indicator defined by Eurostat is adjusted to the country to identify knowledge-intensive activities (KIA). Its application shows the meager penetration in the economic structure given its participation in production (39.2%), as well as in the labor market (30.7%). The two activities that contribute the most to production do not qualify as KIA, nor does the one related to “high technology” and responsible for the main export product. By excluding the branches of Education and Health, the contribution to production is reduced to 27.9%. The low weight of KIAs linked to Science and Technology in employment (18%) and in production (8.3%) hinders the aspirations of competing globally as a knowledge society based on innovation. The KIA are accentuated in the service sector (38.3%); its participation in Manufacturing is minimal (6.3%) and null for the agricultural sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".