When benefits outweigh costs: Reconsidering “automatic” phonological recoding when reading aloud.
Bibliographic record
Abstract
Skilled readers are slower to read aloud exception words (e.g., PINT) than regular words (e.g., MINT). In the case of exception words, sublexical knowledge competes with the correct pronunciation driven by lexical knowledge, whereas no such competition occurs for regular words. The dominant view is that the cost of this "regularity" effect is evidence that sublexical spelling-sound conversion is impossible to prevent (i.e., is "automatic"). This view has become so reified that the field rarely questions it. However, the results of simulations from the most successful computational models on the table suggest that the claim of "automatic" sublexical phonological recoding is premature given that there is also a benefit conferred by sublexical processing. Taken together with evidence from skilled readers that sublexical phonological recoding can be stopped, we suggest that the field is too narrowly focused when it asserts that sublexical phonological recoding is "automatic" and that a broader, more nuanced and contextually driven approach provides a more useful framework.
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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 | 0.002 | 0.019 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".