Understanding the Distraction and Distraction Mitigation Factors and Their Relationship with the Procrastination of Master’s and Doctoral Students in Administration
Bibliographic record
Abstract
The lack of studies on academic procrastination caused by distractions in the context of social isolation, during the COVID-19 pandemic, motivated the study that sought to answer: How do master’s and doctoral students perceive distraction and distraction mitigation factors in about your procrastinating behavior? Aiming to understand the distraction and attention mitigation factors that influence procrastinating behavior in the postgraduate academic context. This is qualitative research of phenomenological nature. The study participants are twenty-four students, twelve master’s students representing about 23% of the universe, and twelve doctoral students, corresponding to approximately 21% of the universe. Based on a literature review, a theoretical framework developed that allowed comparison with the earlier analysis categories and the data collected in the semi-structured interviews. The topics Academic Distraction Factors (ADF) and Academic Distraction Mitigation (ADM) generated 583 citations, finding sixty-one codes or subcodes. The lack of planning and work outside presented as complicating factors that lead to academic procrastination. Suitable time management and the use of tools to help manage learning are good allies in mitigating distractions.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".