Commentary on Studies Citing This Author Concerning Doodling as a Measure of Burnout
Bibliographic record
Abstract
The ability of doodling to act as an indicator of depression and anxiety regarding research burnout is a topic that has seen the publication of six articles by this author since 2021. This commentary aims to determine the extent to which any of these articles have been cited by subsequent researchers in furthering the literature on doodling. The keywords “C Nash Doodling Burnout” were searched through Google Scholar in February 2024 with 142 returns. Only four of these reports included all keywords. Of these returned studies, two were found to add to the literature on doodling substantially, in part as a result of the citations to the work of this author. However, one of these two publications did so while also including a misrepresentation of this author’s work. With few studies on doodling behavior, noting these publications and their limitations represents an important contribution. This work also adds to the paucity of publications by authors examining citations to their work.
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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 | 0.030 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.035 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".