From Task Avoidance to Task Engagement: A Project-Analytic Perspective on the Role of Mood-Repair, Irrational Beliefs and Preference Reversal in Procrastination
Bibliographic record
Abstract
The goal of this research is to examine self-regulation failure in procrastination through affect (i.e., mood-repair process) and maladaptive cognitions (i.e., irrational beliefs).Using Personal Project Analysis (PPA), specific affective and cognitive dimensions of PPA were selected from previous studies to examine mood-repair process and irrational beliefs.My dissertation research consisted of six studies that were quantitative (selfreport questionnaires) and qualitative (interviews) in nature.In the first two studies, I examined the underlying factors of emotions associated with procrastination using a principal component analysis (Study 1a) and a confirmatory factor analysis (Study 1b).Results revealed a 3-factor solution consisting of a single factor of positive affect (e.g., happy, content), and two factors of negative emotions namely frustration intolerance (e.g., frustration, resentment) and fear of failure (e.g., stress, fear of failure).Using these three factors of emotions, I examined two time segments of procrastination in the subsequent studies -the procrastination episodes (i.e., episodes when they needlessly delayed their academic task) and the last-minute effort episodes (i.e., episodes when they started working on their academic task).In Study 2, I took a dual-process perspective to examine the interplay of emotions and cognitions during the procrastination episodes. Results of the quantitative (Study 2a) and qualitative (Study 2b) revealed strong supportfor the temporal mood-repair model of procrastination, and the idea that mood-repair and irrational justifications is associated with the delay of academic tasks.In Study 3, I investigated preference reversal, that is, why students move from not taking actions on their academic task during the procrastination episodes to taking actions near the deadlines, through the lens of emotions.Results of the quantitative (Study 3a) and v Table of Contents Abstract………………………………………………………………………… ii Acknowledgement……………………………………………………………... iv Table of contents……………………………………………………………….. v List of Figures………………………………………………………………….. xi List of Tables…………………………………………………………………… xiv List of Appendices……………………………………………………………... xviii Introduction……………………………………………………………………..
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".