Bibliographic record
Abstract
This article investigates the role of Brazilian legislation in the (re)production of subordinate forms of incorporation of peripheral countries in global value chains (GVCs) through new dynamics for the extraction of value intermediated by international systems of investment. The purpose is to show how financial deregulation contributes to “putting-out” research and development (R&D) labs of major brands, and serves as a way of exploring casual and flexible hiring schemes for skilled workers. To this end, the article explores the financing policies for the Brazilian innovation system, whose aim is to promote the inclusion of higher value-added activities in the GVCs through the connection of national innovative micro-enterprises to direct financial investments, a strategy considered fundamental to boost the country’s industrialisation. The analysis focuses on two Calls for Funds for technology-based start-ups, a category of micro-business where investments have been stimulated due to such policies. The results reveal how leading companies in global value chains have been using this rentier development model to reduce costs in R&D activities by sharing the risks of innovation and transferring labour charges to start-ups, advancing the casualisation of work for skilled workers. In this context, casual employment with no labour rights has turned countries at a low level of industrialisation, like Brazil, into an attraction to the dynamics of the CGVs. KEYWORDS: start-ups; global value chains; production funding; casualisation of work; putting-out system
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".