A Study on CPH and Debate Summary in FLL
Bibliographic record
Abstract
The optimal age in FLL (foreign language learning) for children has been discussing over 50 years but there is no satisfactory conclusion for us. However, the notion “the younger, the better” in FLL has a big market in the world. As a result, the distorted hypothesis is being spreading widely as a true and complete theory. Specifically speaking, it’s caused by the confusion on the concepts of “second language and foreign language”, “learning and acquisition”, “CPH (critical period hypothesis)”. Therefore, based upon the discussion of theoretical foundation of linguistics, psychology and physiology in FLL for children, the environment of FLL and the importance of mother tongue, all of us will have a complete knowledge of the concept. By analyzing the deep reasons on the tendency of lowering the age in FLL both at home and abroad, those blind followers who are still being misled will have a rational attitude towards FLL. Hence, the rational deeds of “a language can be taught from any age upwards” will definitely go into the heart of everyone.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".