Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".