Mapping cognition across lab and daily life using experience-sampling
Bibliographic record
Abstract
This dataset contains 8459 multidimensional experience-sampling (mDES) probes from a sample of 370 different subjects in lab and daily life settings across Canada and the UK, collected from 2017 to 2022. mDES data includes 9 items as well as data regarding subjects' activity, social environment, and physical location at the time of probing. This dataset is an aggregation of 5 previously published datasets, referenced in the 'dataset' column of the dataframe. The datasets' previous publications are: Konu et al., 2020 ("konu2020"): mDES during a Go/No-Go task in fMRI in the UK. https://doi.org/10.1016/j.neuroimage.2020.116977 Ho et al., 2020 ("mckeown_pre"): mDES during daily life in the UK before the COVID lockdown. https://doi.org/10.1016/j.neuroimage.2020.116765 Konu et al., 2021 ("konu2021"): mDES during a battery of cognitive tasks in a lab setting in the UK https://doi.org/10.1016/j.concog.2021.103139 Mckeown et al., 2021("mckeown_post"): mDES during daily life in the UK during the COVID lockdown. https://doi.org/10.1073/pnas.2102565118 Mulholland et al., 2023 ("mulholland"): mDES during daily life in Canada after the COVID lockdown had been lifted. https://doi.org/10.1016/j.concog.2023.103530
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".