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
Comprehending speech in our native language is an impressionistically effortless and routine task. We often give little consideration to its complexity. Only in particularly challenging situations (e.g., in noisy environments, when hearing significantly accented speech) do some of these intricacies become apparent. Higher-order knowledge constrains sensory perception and has been demonstrated to play a crucial role in other domains of human language processing. Moreover, incorporating measures of brain activity during online speech comprehension has just begun to highlight the extent to which top-down information flow and predictive processes are integral to sensory perception. This review argues that our phonological system, at a relatively abstract level, is one such source of higher-order knowledge. In particular, I discuss the extent to which phonological distinctive features play a role in perception and predictive processing during speech comprehension with reference to behavioral and neurophysiological data. This line of research represents a tractable linking of linguistic theory with models of perception and speech comprehension in the brain.
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 | 0.005 |
| 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