Time Budgets, Diaries, and Analyses of Concurrent Practice Activities
Bibliographic record
Abstract
Introduction Time is an inescapable dimension of all human activity. What time of day, month, and year, for how long, before or after what other activity, how long before or after another given activity and how often, are questions answerable for all activities. The relevance of each question varies with one's perspective on the activity. Time-use methodology can provide rich, objective, and replicable temporal information to answer the questions posed, hence providing a basis for forming and/or collaborating empirical judgments. Coupled with other objective and subjective contextual information on each incident of an activity, time-use methodologies can generate invaluable information for understanding activities and human behavior. Time-use studies show how people use their time. Minimally, they show what activities people do, while maximally, they can show what people are doing, where they are, who they are with, and how they feel. Time-use studies can use a variety of data-collection methods ranging from self-reported activities to observation reports. In expertise research, time spent in an activity needs to be considered at a minimum of two different levels: a macro and a micro level. These two different levels encompass different units of time and provide different information about an activity. For example, at a macro level a researcher interested in music expertise may want to assess a typical week of training by analyzing time spent on general activities, such as practice alone, practice with a teacher, playing with others, resting, and so forth.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".