MétaCan
Menu
Back to cohort
Record W3114165119

The Philippine Economy During the COVID Pandemic

2020· preprint· en· W3114165119 on OpenAlexaboutno aff
Joseph Anthony Lim

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsStimulus (psychology)DefaultRecessionUnemploymentCoronavirus disease 2019 (COVID-19)PandemicLoanQuarter (Canadian coin)Economic recoveryGovernment (linguistics)EconomicsFinancial crisisDevelopment economicsEconomic policyBusinessPolitical scienceEconomic growthFinanceMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper describes and analyzes the impact of the COVID pandemic and the subsequent hard and mild lockdowns on the Philippine economy at various stages from March, 2020 to early September, 2020. The COVID pandemic and resulting hard lockdown (Enhanced Community Quarantine) from March 17, 2020 to May 31, 2020 had resulted in the highest unemployment and biggest fall in Philippine GDP on the second quarter of 2020. The paper shows 90% of the labor force was affected by this hard lockdown. Bayanihan Acts 1 and 2 are the biggest Social Amelioration Program (SAP) ever legislated and implemented by the Philippine government. The paper discusses the need for a bill to prevent the danger of massive loan defaults, bankruptcies and potential financial crisis resulting from the deep recession. The paper goes on to discuss the debate between more conservative economic managers, on one hand, and legislators and NGOs who want a stronger and more encompassing fiscal stimulus to the distressed economy, on the other. It ends with a discussion on the crux of the debate, which is financing the fiscal deficits that will arise due to the pandemic and the economic stimuli.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0010.009
Insufficient payload (model declined to judge)0.0000.000

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 head0.163
GPT teacher head0.479
Teacher spread0.316 · 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 teacher head, not a consensus.

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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicGlobal Health and EpidemiologyFrench-language works237,207