Measuring people intensively.
Bibliographic record
Abstract
An overview is provided of measures that are administered repeatedly in daily life. Variations of thismethodology have been referred to as ecological momentary assessment, diary methods, daily processmeasures, and most broadly as intensive repeated measures in naturalistic settings (IRM-NS). Contrastsare drawn between IRM-NS methods on the basis of different sampling strategies, such as time-contingent recording, signal-contingent recording, and event-contingent recording. Common threats tothe internal validity, construct validity, and external validity of IRM-NS measures are reviewed, alongwith ways to reduce these threats. The statistical analysis of IRM-NS data is considered, with a particularfocus on the investigation of intraindividual variability. An extended example is provided of an IRM-NSmeasure, an event-contingent recording method for the assessment of interpersonal behaviour.Keywords: ecological momentary assessment, daily diary methods, naturalistic assessments, IRM-NSvalidity, IRM-NS reliability
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".