Bibliographic record
Abstract
Given that Chinese writing conventions lack inter-word spacing, understanding whether and how readers of Chinese segment regular unspaced Chinese writing into words is an important question for theories of reading. This study examined the processing outcomes of introducing spaces to written Chinese sentences in varying positions based on native speaker consensus. The measure of consensus for every character transition in our stimuli sentences was the percent of raters who placed a word boundary in that position. The eye movements of native readers of Chinese were recorded while they silently read original unspaced sentences and their experimentally manipulated counterparts for comprehension. We introduced two types of spaced sentences: one with spaces inserted at every probable word boundary (heavily spaced), and another with spaces placed only at highly probable word boundaries (lightly spaced). Linear mixed-effects regression models showed that heavily spaced sentences took identical time to read as unspaced ones despite the shortened fixation times on individual words (Experiment 1). On the other hand, reading times for lightly spaced sentences and words were shorter than those for unspaced ones (Experiment 2). Thus, spaces proved to be advantageous but only when introduced at highly probable word boundaries. We discuss methodological and theoretical implications of these findings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".