Editorial: New perspectives on procrastination, volume II
Bibliographic record
Abstract
The field of procrastination is growing almost exponentially, with the academic search engine Semantic Scholar indicating approximately half of the more than 16,000 articles written on the topic were published in the previous 6 years. At this point, review articles of the field are becoming increasingly important. In addition, foundational issues still are being addressed, in particular the diagnosis and assessment of procrastination and its effects. More sophisticated techniques are being increasingly employed, with hierarchical or multi-level modeling proving particularly useful. Finally, a mature science increasingly moves into intervention or treatment, building on the previous components. Each of these components are featured in this Research Topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".