Flexible R&D Promotion Instruments as a Way to Develop the Space Industry in Brazil
Bibliographic record
Abstract
The space sector is technology-intensive with high-cost, risky and long-term projects. Encouraging technological development in the sector therefore requires specific policies capable of overcoming the sector’s characteristics. Due to budgetary issues or rules that imposed greater accountability on the public manager in case of failure of technological development projects, the Brazilian space sector could not use the existing instruments for financing R&D. However, with the change in the National Innovation Law and the introduction of the technological ordering instrument (ETEC), this type of instrument began to be used by the Brazilian Space Agency in the contracting of technological projects. ETEC is a milestone, as it allows the public sector to accept that the development project may not be successful, without the public manager being held responsible for the failure. And the release of budgetary resources from the National Fund for Scientific and Technological Development allowed the adoption of subsidy notices to encourage innovation directly by private companies, a drastic historical change, since space investments were carried out through the use of public research institutes. The use of new instruments opens up new possibilities for direct incentives for the Brazilian space industry.
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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 | 0.006 | 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".