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Record W3095049482 · doi:10.1080/0376835x.2020.1834351

The contribution of services to international trade in Southern Africa

2020· article· en· W3095049482 on OpenAlexaboutno aff
Justin Visagie, Ivan Turok

Bibliographic record

VenueDevelopment Southern Africa · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersUnited Nations University World Institute for Development Economics Research
KeywordsInternational tradeTrade in servicesProductivityBusinessQuarter (Canadian coin)Market accessEconomicsInternational economicsValue (mathematics)Trade barrierEmerging marketsEconomic growthAgricultureGeographyFinance

Abstract

fetched live from OpenAlex

Services are the fastest growing portion of world trade and now account for nearly a quarter of global exports. This presents opportunities for emerging economies to adapt and enter new markets. Many countries in southern Africa have struggled to diversify from a heavy reliance on primary commodities towards manufacturing industries. Tradable services could contribute to economic growth and development by bolstering industrial capabilities, facilitating productivity growth, and contributing directly to exports. We examine evidence on international services trade for the Southern African Development Community between 1995 and 2012. Tradable services appear to have made a limited contribution to total trade for most countries, and there is little evidence of significant regional integration or specialisation in higher value-added activities. The role of tradable services is an important policy and research agenda that warrants much more attention all round.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.195
Teacher spread0.154 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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