Bibliographic record
Abstract
Sketching is a tool that people can use without any training and benefit from when communicating, thinking or keeping records. The wide range of uses of sketching has made it a high-potential, promising research topic for human-computer interaction researchers. The first step for the researchers who were working for this purpose was developing sketch recognition models. However, in order to continue these studies, they needed a large amount of sketch data. Creating these datasets is a costly task. For this reason, the cheapest methods that enable to produce a large number of sketches quickly were preferred in the research. Although the required amount of sketching data has been collected thanks to these methods, it is necessary to question their quality and similarity to the sketches created during daily life interactions. In this article, a critical comparison of the most widely used sketch datasets in the literature with the sketches we create during daily life interactions is made. In addition, a new dataset which consists of sketches that are created during human-human interactions is introduced. The study showed that popular sketch datasets do not reflect the quality of sketches we create in our daily life.
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 | 0.016 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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".