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Record W4376649343 · doi:10.18235/0004902

Caribbean Economics Quarterly: Volume 12, Issue 1: Reflections on Innovation and Productivity as Caribbean Businesses Emerge from the Pandemic

2023· report· en· W4376649343 on OpenAlexfundno aff
David H. Rosenblatt, Diether Beuermann, Henry Mooney, Khamal Clayton, Sylvia Döhnert, Víctor Gauto, Monique Graham, Gisele Teixeira, Nirvana Satnarine-Singh

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Zones and Regional Development
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsProductivityGeneral partnershipPandemicEconomicsCoronavirus disease 2019 (COVID-19)Development economicsEconomic geographyBusinessEconomic growthFinance

Abstract

fetched live from OpenAlex

This edition of the Caribbean Economics Quarterly focuses on the issue of long-term economic growth. As economies have recovered from the pandemic, the main question is: Will the region return to the slow long-run growth of the pre-pandemic period? The key lies in innovation and productivity. To understand the challenges facing businesses to innovate and increase productivity, the authors draw on recent firm-level data from the Compete Caribbean Partnership Facilitys Innovation, Firm Performance and Gender (IFPG) survey. This edition starts with an overview of past performance of Caribbean countries in terms of economic growth and productivity. It then describes the IFPG data and summarizes recent research papers analyzing those data and the conclusions emerging from that research. Finally, the country chapters draw on the Compete Caribbean database to describe the challenges facing firms at the country level.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0030.002
Scholarly communication0.0100.005
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0170.003

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 head0.173
GPT teacher head0.313
Teacher spread0.140 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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