Letter detection for homographs with different meanings in different language texts
Bibliographic record
Abstract
Tests of inter-lingual homographs that have different meanings across two languages support models postulating initial non-selective access to competing language representations, e.g. Bilingual Interactive Activation (BIA) model. Most such research assessed inter-lingual homographs in the absence of connected text. Here a letter detection paradigm was used that required subjects to detect letters in words in connected text. Prior work with this paradigm suggested that readers respond to only one interpretation of an intra-lingual homograph when detecting letters. Three experiments described here indicate that letter detection patterns to inter-lingual homographs are similar, i.e. detection reflects only a context appropriate interpretation. However, the demonstration that text role, text cohesiveness and bilingual fluency affect inter-lingual letter detection (Experiments 1 and 2), and that word role affects detection even though target frequency is constant across inter-lingual meanings (Experiment 3) indicates that selectivity is in response to post-lexical processes. Thus, results are seen as compatible with tenets of the BIA model.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".