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Record W4210424937 · doi:10.15173/glj.v13i1.4504

Chasing Funds: Start-ups from a Global Value Chains Approach

2022· article· en· W4210424937 on OpenAlexvenueno aff
Simone Wolff

Bibliographic record

VenueGlobal Labour Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsCasualIndustrialisationInvestment (military)Value (mathematics)BusinessContext (archaeology)LegislationProduction (economics)Work (physics)DeregulationMarket economyEconomicsIndustrial organizationEconomic systemPolitical science

Abstract

fetched live from OpenAlex

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

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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.

Study designTheoretical or conceptual
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

Citations2
Published2022
Admission routes1
Has abstractyes

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