The PSR corpus: A Persian sentence reading corpus of eye movements
Bibliographic record
Abstract
The present study introduces the Persian Sentence Reading (PSR) Corpus, aiming to expand empirical data for Persian, an under-investigated language in research on oculomotor control in reading. Reading research has largely focused on Latin script languages with a left-to-right reading direction. However, languages with different reading directions, such as right-to-left and top-to-bottom, and particularly Persian script-based languages like Farsi and Dari, have remained understudied. This study pioneers in providing an eye movement dataset for reading Persian sentences, enabling further exploration of the influences of unique Persian characteristics on eye movement patterns during sentence reading. The core objective of the study is to provide data about how word characteristics impact eye movement patterns. The research also investigates the characteristics of the interplay between neighboring words and eye movements on them. By broadening the scope of reading research beyond commonly studied languages, the study aims to contribute to an interdisciplinary approach to reading research, exemplifying investigations through various theoretical and methodological perspectives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".