Bibliographic record
Abstract
In the digital age, people's self-perception of productivity tends to diverge from reality, with some overestimating and others underestimating their output. This study analyzed 30,000 behavioral and demographic records to predict such perception gaps. By establishing a threshold based on half a standard deviation in the perception gap, three categories were identified: Overestimators, Underestimators, and Accurate assessors. Class imbalances were addressed through Synthetic Minority Over-sampling Technique (SMOTE), and four classifiers-Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and XGBoost-were evaluated using macro and weighted Fl-scores. Logistic Regression achieved the best performance with a macro Fl-score of 0.35 and a weighted Fl-score of 0.50, outperforming more complex models while remaining interpretable. Feature importance identified job type and digital preferences for social media as the strongest predictors, with workers in the fields of finance, health, and IT, as well as frequent users of Instagram or TikTok, demonstrating overestimation rates, in particular. These findings highlight that internal behavioral characteristics, rather than external cues alone, drive productivity misperception and also propose interventions based on contexts of occupation and digital usage habits that could enhance productivity awareness as well as digital well-being.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".