Bibliographic record
Abstract
COVID-19 Crisis and the Informal Economy is a WIEGO-led 12-city longitudinal study that assesses the impact of the COVID-19 crisis on specific groups of Informal Workers and their households. Using a survey questionnaire and in-depth interviews, Round 1 assessed the impact of the crisis in April 2020(the period of peak restrictions in most cities) and in June/July 2020 (when restrictions had eased in most cities) in comparison to December 2019 (pre-COVID-19). Round 2 will assess continuing impacts versus signs of recovery in the first half of 2021, compared to the pre-COVID-19 period and Round 1. This report presents the summary findings of Round 1 of the study in Bangkok, Thailand. Researchers in Bangkok surveyed and interviewed home-based workers, domestic workers, street vendors, and motorcycle taxi drivers whose organizations are affiliates of the Federation of Informal Workers of Thailand (FIT)1, as well as massage therapists from Jaravee Association for the Conservation of Thai Massage, and waste pickers from Poonsap Community in Sai Mai District and Soi Sua Yai Uthit in Chatuchak District.
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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".