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Record W4379034951 · doi:10.5089/9781513587219.071

South Asia's Path to Resilient Growth

2022· book· en· W4379034951 on OpenAlexaff
Ranil Salgado, Klaus-Peter Hellwig, Mercedes García-Escribano, Tewodaj Mogues, Marian Moszoro, Mauricio Soto, Ruchir Agarwal, Andrew Hodge, Racha Moussa, Piyaporn Sodsriwiboon, Jarkko Turunen, Bazlul Haque Khondker, Emmanouil Kitsios, Sanghamitra Mukherjee, Vybhavi Balasundharam, Patrick Blagrave, Eugenio Cerutti, Ragnar Gudmundsson, Weicheng Lian, Fei Liu, Katsiaryna Svirydzenka, Biying Zhu, Shanaka Peiris, Pragyan Deb, Naihan Yang, Olivier Bizimana, Laura Jaramillo, Saji Thomas, Jiae Yoo, Chetan Ghate, Faisal Ahmed, Gerard Almekinders, Sumiko Ogawa, Kristalina Georgieva

Bibliographic record

Venuenot available
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsQueen's University
FundersHarvard Kennedy SchoolInternational Labour OrganizationEconomic Research InstituteUniversity of CambridgeHarvard UniversityPennsylvania State UniversityJohns Hopkins UniversityUniversity of Pennsylvania
KeywordsPath (computing)GeographySouth asiaComputer scienceAncient historyHistoryComputer network

Abstract

fetched live from OpenAlex

South Asia’s Path to Resilient Growth highlights the remarkable development progress in South Asia and how the region can advance in the aftermath of the COVID-19 pandemic. Steps include a renewed push toward greater trade and financial openness, while responding proactively to the distributional impact and dislocation associated with this structural transformation. Promoting a green and digital recovery remains important. The book explores ways to accelerate the income convergence process in the region, leveraging on the still-large potential demographic dividend in most of the countries. These include greater economic diversification and export sophistication, trade and foreign direct investment liberalization and participation in global value chains amid shifting regional and global conditions, financial development, and investment in human capital.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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
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

Citations11
Published2022
Admission routes1
Has abstractyes

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