Letter detection for homographs with different meanings in different language texts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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 it