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Record W3180148390 · doi:10.5772/intechopen.96648

Chile in Times of Pandemic

2021· book-chapter· en· W3180148390 on OpenAlexaboutno aff
Daniela Araya Ramírez

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Relations in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsPosition (finance)Economic policyConsumption (sociology)EconomicsPoliticsForeign direct investmentFiscal policyInvestment (military)Quarter (Canadian coin)PandemicPrivate consumptionDevelopment economicsPrivate sectorCoronavirus disease 2019 (COVID-19)Political scienceEconomic growthGeographyMacroeconomicsFinance

Abstract

fetched live from OpenAlex

This chapter provides an overview of the economic impact in Chile caused by Covid −19, the evolution of the Chilean economy in a compact and direct view. The restrictions imposed on consumption and production, as well as the decline of private investment are be the main obstacles that will keep the country from growing, exacerbated by the consequences on labor activity; on the other hand, the increase in public spending helps to counteract these effects, but only to a certain extent. We will consider a brief look at the political situation in the country, since the Chilean economy was in a vulnerable position at the time of receiving the pandemic with an economic contraction of 4.1% in the last quarter of 2019, as the result of the social crisis triggered in October of the mentioned year. The following aspects will be developed which include quantitative information about; Chilean economic situation, making reference and taking comparative parameters to the foreign sector, public sector, fiscal policy and monetary policy. Economic prospects and political situation.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.003

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 head0.030
GPT teacher head0.330
Teacher spread0.300 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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