Bibliographic record
Abstract
The anxiousness of economic sectors in the Philippines heightened when the GDP declined to 16.5%in the second quarter of 2020. This paper aimed to reflect the economic effect of the Covid 19pandemic accounting for the number of cases during and after lockdown. It identified key economicsectors both affected and not by the pandemic. Researchers employed data mining and descriptivemethods with the aid of horizontal and vertical analysis of the Philippine GDP to describe thePhilippine economy during the period of community quarantine. The economic impact of COVID tothe regions were described using the distance formula. For better illustration of the results, theresearchers employed Gephi software. Findings revealed that the information and communication,financial and insurance activities, and public administration and defense which includes thecompulsory social services were the economic sectors not affected by the pandemic despite the policyon partial or non-opening of business centers. On the other hand, the greatly hit economic sectorsduring a pandemic are transportation and communication, accommodation and food service activities,and other services. These sectors positively affected all other sectors of the economy except for agrihunting, forestry, and fishing, information and communication, financial and insurance activities, andpublic administration and defense: compulsory social activities. The Philippine economy will continueto decline when these hard-hit sectors cannot recover. The economic measures were highest in NCRhaving the maximum risk of economic loss and COVID 19 infection. Region 3 and Region 4A wereidentified to contribute most to the economic loss other than NCR. The safety measures are stringentto the identified regions but these measures may not be applied to the rest of the Philippine regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".