Tutorial data for the article "Handling Planned and Unplanned Missing Data in a Longitudinal Study" [2020, Canada]
Bibliographic record
Abstract
[ENG] This dataset contains the data used in the tutorial article "Handling Planned and Unplanned Missing Data in a Longitudinal Study", in press at "The Quantitative Methods for Psychology". It contains a subset of longitudinal data collected within the context of a survey about COVID-19 (data on sleep and emotions). This dataset is intended for tutorial purposes only. With the observations and variables in this dataset, the analyses presented in the tutorial can be reproduced. For more information, see de la Sablonnière et al. (2020). [FRE] Ce jeu de données contient les données utilisées dans l'article tutoriel "Handling Planned and Unplanned Missing Data in a Longitudinal Study", sous presse à "The Quantitative Methods for Psychology". Il contient un sous-ensemble de données longitudinales collectées dans le cadre d'une enquête sur le COVID-19 (données sur le sommeil et les émotions). Cet ensemble de données est destiné à des fins didactiques uniquement. Avec les observations et les variables de ce jeu de données, les analyses présentées dans le tutoriel peuvent être reproduites. Pour plus d'informations, voir de la Sablonnière et al. (2020).
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.205 | 0.163 |
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".