A global experience‐sampling method study of well‐being during times of crisis: The CoCo project
Bibliographic record
Abstract
Abstract We present a global experience‐sampling method (ESM) study aimed at describing, predicting, and understanding individual differences in well‐being during times of crisis such as the COVID‐19 pandemic. This international ESM study is a collaborative effort of over 60 interdisciplinary researchers from around the world in the “Coping with Corona” (CoCo) project. The study comprises trait‐, state‐, and daily‐level data of 7490 participants from over 20 countries (total ESM measurements = 207,263; total daily measurements = 73,295) collected between October 2021 and August 2022. We provide a brief overview of the theoretical background and aims of the study, present the applied methods (including a description of the study design, data collection procedures, data cleaning, and final sample), and discuss exemplary research questions to which these data can be applied. We end by inviting collaborations on the CoCo dataset.
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 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) | 0.000 | 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) | 0.000 | 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".