Bibliographic record
Abstract
My aim is to discuss the ontogenesis of structured patterns of pertinent phonemic differences of a given sociolinguistic variety and the difficulties children face when trying to learn how to read. I first explain how the child innately guided loses her/his sensitivity to some phonetic features, realigns categories and sharpens or broadens categories in such a way that the cortex cells tune with categories that are pertinent to the sociolinguistic variety being acquired. Then, I focus on learning an alphabetic system like the Latin script, as a cognitive process, based on neurosciences findings about the reading process. I explain the initial difficulties children face, when trying to learn how to read. Before knowing the principles of the alphabetic system, the child does not perceive the contrasts among the syllable constituent units. The difficulty of delimiting words, namely unstressed words, and the fact that vision neurons of primates are genetically programmed for disregarding the minimal differences among basic features and the differences of direction such as right as opposed to left, and of vertical position, the bottom, as opposed to the top deserves an adequate early literacy education. Psycholinguistic research today can help overcome those difficulties by applying its results on developing new methods and new teaching materials intended for beginners in the literacy process.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".