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Record W4239182967 · doi:10.1108/oxan-db203306

Long- and short-term threats undermine Taiwan economy

2015· other· en· W4239182967 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2015
Typeother
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantageQuarter (Canadian coin)Private consumptionFellStock marketEconomicsPessimismPresidential systemMedium termReal gross domestic productEconomyConsumption (sociology)Stock (firearms)ChinaGovernment (linguistics)Development economicsEconomic policyBusinessPolitical scienceMonetary economicsGeographyMacroeconomicsFiscal policy

Abstract

fetched live from OpenAlex

Subject The (gloomy) economic outlook for Taiwan. Significance Taiwan's exports fell 8.8% year-on-year during the January-August period, data showed on September 7. Exports, which equal around 60% of GDP, have now fallen for seven consecutive months. GDP growth plummeted to 0.52% in the second quarter of 2015, from 3.84% in the first. The government forecasts 1.56% growth for the calendar year 2015, the lowest since 2010. Impacts The pessimistic export outlook will dampen Taiwan's stock market performance, especially for manufacturing companies. Downward pressure on wages will restrain people's propensity to consume, dragging down growth prospects for private consumption. Economic sluggishness will put the ruling party, the Kuomintang, at a disadvantage in January's presidential election.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0380.008

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 head0.042
GPT teacher head0.328
Teacher spread0.287 · 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
GenreCommentary

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
Published2015
Admission routes1
Has abstractyes

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