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Record W4390024921 · doi:10.5539/ijef.v16n2p25

Flexible R&D Promotion Instruments as a Way to Develop the Space Industry in Brazil

2023· article· en· W4390024921 on OpenAlexvenueno aff
D. P. Szimanski, Michele Cristina Silva Melo, Andrea Felippe Cabello, Lúcia Helena Michels Freitas

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityPromotion (chess)IncentivePrivate sectorPublic sectorSubsidyAgency (philosophy)Space (punctuation)BusinessTechnological changeMilestoneFinanceEconomicsPublic administrationEconomic growthPolitical scienceMarket economyEconomySociologyPoliticsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.290
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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