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
abstract In contrast to arbitrariness, a recent perspective is that words contain both arbitrary and iconic aspects. We investigated iconicity in word recognition, and the possibility that iconic words have special links between phonological and semantic features that may facilitate their processing. In Experiment 1, participants completed a lexical decision task (“Is this letter string a word?”) including words varying in their iconicity. Notably, we manipulated stimulus presentation conditions such that the items were visually degraded for half of the participants; this manipulation has been shown to increase reliance on phonology. Responses to words higher in iconicity were faster and more accurate, but this did not interact with condition. In Experiment 2 we explicitly directed participants’ attention to phonology by using a phonological lexical decision task (“Does this letter string sound like a word?”). Responses to words that were higher in iconicity were once again faster. These results demonstrate facilitatory effects of iconicity in lexical processing, thus showing that the benefits of iconic mappings extend beyond those reported for language learning and those argued for language evolution.
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) | 0.002 | 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