To see or not to see: The roles of item properties and language knowledge in Chinese missing logographeme effect
Bibliographic record
Abstract
Abstract This study examined how language knowledge and item properties (i.e., semantic relatedness and position) influenced Chinese missing logographeme effects. Eighty-four Chinese readers and 53 English readers were asked to search for the Chinese logographeme 口 while reading a Chinese prose passage. The target 口 appeared in five different positions (i.e., left, right, top, bottom, or inside), varying its degree of semantic relatedness to its embedded characters. The generalized linear mixed-effect model revealed a significant interaction between semantic relatedness and position in Chinese, but not in English, readers when visual complexity and frequency were controlled. For Chinese readers, a higher omission rate occurred when 口 appeared in the top and inside positions and exhibited low semantic relatedness with its embedded characters, whereas 口 was omitted more when it was positioned on the right and exhibited high semantic relatedness to its embedded characters. English readers exhibited a different omission pattern: 口 was omitted more when it appeared in the left or right position irrespective of semantic relatedness. In addition, 口 was omitted more in the inside, rather than the bottom, position. These findings suggest that the omission rate of the logographeme is determined by item properties at the sublexical level and the reader’s language knowledge.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".