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Record W2603386640 · doi:10.18235/0000616

Exploring Firm-Level Innovation and Productivity in Developing Countries: The Perspective of Caribbean Small States

2017· book· en· W2603386640 on OpenAlexfundno aff
Gustavo Crespi, Sylvia Döhnert, Alessandro Maffioli, Antônio Marcos Höelz Pinto Ambrózio, Manuel Barron, Federico Bernini, Lucas Figal Garone, Kayla Grant, Preeya Mohan, Diego Morris, Roberta Rabellotti, Filipe Lage de Sousa, Eric Strobl, Patrick Kent Watson

Bibliographic record

VenueInter-American Development Bank eBooks · 2017
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
FundersUniversity of SussexInternational Development Research Centre
KeywordsPerspective (graphical)ProductivityEconomic geographyBusinessGeographyRegional scienceEconomicsEconomic growthComputer science

Abstract

fetched live from OpenAlex

This monograph explores productivity, innovation, and firm performance, important issues that affect private sector development in the Caribbean region. Using unique and recently available datasets consisting of more than 4,000 surveys at the firm level, and covering 13 Caribbean countries, it examines a set of variables that affect productivity and innovation in the region. The chapters provide a unique perspective on the barriers to innovation, and on how access to finance, competition, foreign direct investment, gender, access to electricity, and public programs affect productivity and innovation at the firm level. The publication culminates with a review of the policy interventions that could have the most impact in increasing firm performance within the region.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.147
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.013
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.143
GPT teacher head0.257
Teacher spread0.114 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2017
Admission routes1
Has abstractyes

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