Book of Abstracts of the 21th European Conference on Eye Movements in Leicester 2022
Bibliographic record
Abstract
Contents Keynotes: Iain Gilchrist: Integrative Active Vision p 5 Ziad Hafed: A Vision for orienting in Primate Oculomotor Control Circuitry p 6 Fatema Ghasia: Miniscule Eye Movements Play a Major Role in Binocular Vision Disorders p.7 Miriam Spering: Eye Movements as a Window into Human Decision-Making p.8 Monica S. Castelhano: Explorations of how Scene Context and Previous Experience Dynamically Influence Attention and Eye Movement Guidance p.9 Symposia: Eye Tracking and the Visual Arts p.19 Eye Movements during Text Processing and Multiline Reading p.23 Unstable Fixation and Nystagmus with a Focus on the Next Generation of Researchers p.84 Eye Movements as a measure of Higher-Level Text Processing p.97 Eye Movements in Memory Processes Between Working Memory and Long-Term Memory p.178 Symposium to Honour Alexander Pollatsek’s Legacy to Eye Movement Research p.204 Talks: Reading p.30 Parafoveal Processing p.36 Cinical and Applied p.39 Visual Search p.92 Eye Movement Control in Reading I & II p.104 & 116 & 225 Reading Development p.110 Decision-Making p.122 Eye-tracking Methods p.128 Real World and Virtual Reality p.134 Chinese Reading p.185 Special Populations p.191 Visuo-motor p.195 Bilingual Reading p.201 & 217 Reading Comprehension p.219 Pupillometry p.235 Poster sessions: Attention p.44 & 139 Cognition p. 49 Visuo-Motor p.62 Memory p.145 Methods p.150 Reading p. 57 & 155 Real World p.169 Social Cognition p.173
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.534 | 0.307 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".