on the NYSOA King-Devic Saccadic Test
Bibliographic record
Abstract
Many studies have been done on the connection between students ' eye movements and educational performance. From these studies have emerged several clinical measuring tools for optometric use. One such test, widely used in the United States, Canada, and Australia, is the New York State Optometric Association King-Devic (NYSOA KD) test. This test is easy to administer and has been incorporated in many visual screening protocol used by non-professionals. The test is also used in formal investigations into understanding learning-related visual problems. The norms derived from the early studies were done on children in the United States. In order to use this test in Denmark we wanted to know if the norms derived from studying American children could be used directly or if new norms for Danish children must be established. Our hypothesis is that Danish children would perform the same as age-matched American children. To test this, we obtained permission to work with random samples of students from three different schools. Due to time and personnel restrictions and to allow the size of our groups to be large enough to allow formal comparisons with the US data, we chose to test only four different age groups. We worked with 6-, 9-, 12- and 14-year-olds. Our testing
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".