An Exploratory Study on Personal Finance in the Context of COVID-19
Bibliographic record
Abstract
The world is currently experiencing a dramatic crisis that has not yet reached bottom. In Mexico, in the second quarter of 2020, there was a drop in the gross domestic product of 18.9% compared to the same quarter of 2019. In this context, the objective is to identify types of personal expenses in households located in Culiacán, Sinaloa, Mexico as of July 15, 2020. The main results were that most of the respondents' budgets spend according to their income, have had no problem paying their bank loans on time, would consider a fund for future contingencies, have not purchased health insurance, have not bought a computer or cell phone, among other issues analyzed. The main findings are oriented to the fact that the studied population has not acquired additional medical insurance despite the pandemic. It is also concluded that the population under study has become aware of having savings for contingency funds and that digital life still shows resistance in making personal financial decisions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".