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
This study starts from the hypothesis, first advanced by McDonald and Shillcock (2001), that the word frequency effect for a large part reflects local syntactic co-occurrence. It is shown that indeed the word frequency effect in the sense of pure repeated exposure accounts for only a small proportion of the variance in lexical decision, and that local syntactic and morphological co-occurrence probabilities are what makes word frequency a powerful predictor for lexical decision latencies. A comparison of two computational models, the cascaded dual route model (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001) and the Naive Discriminative Reader (Baayen, Milin, Filipovic Durdjevic, Hendrix, & Marelli, 2010), indicates that only the latter model properly captures the quantitative weight of the latent dimensions of lexical variation as predictors of response times. Computational models that account for frequency of occurrence by some mechanism equivalent to a counter in the head therefore run the risk of overestimating the role of frequency as repetition, of overestimating the importance of words’ form properties, and of underestimating the importance of contextual learning during past experience in proficient reading.
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 | 0.001 | 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 | 0.001 |
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 it