Research Methodologies for Multitasking Studies
Bibliographic record
Abstract
The research on multitasking is scattered across disciplines, and the definitions of multitasking vary according to the discipline. As a result, the research is not coherent nor consistent in the approaches taken to understanding this phenomenon. In this chapter, the authors review studies on multitasking in different disciplines with a focus on the research methodologies used. The three main research paradigms (empirical-analytical, interpretive, and critical) are used as a framework to understand the nature of the research. The strengths and weaknesses of the research in each of the paradigms are examined, and suggestions are made for utilizing different research methodologies to bring clarity to the research in this field. Such an endeavour will help to build interdisciplinary and multidisciplinary research and help guide future research and theory building.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".