Bibliographic record
Abstract
Differences between younger and older learners It is commonly assumed that young children learn languages better than older ones. This is largely based on the observation that in immigrant families, young children normally become – apparently effortlessly – highly proficient in the new language. Research on children who did not have the opportunity to acquire language at an early age (‘wolfchildren’, or congenitally deaf children who recovered their hearing at a late age) indicate that they found it very difficult to learn it later. According to the critical period hypothesis (CPH), young children have a natural ability to learn languages which deteriorates when they get older; though when exactly this critical period ends is a subject of debate. The CPH has been the subject of much controversy and criticism (see, for example, Marinova-Todd et al., 2000). In any case, the evidence summarized above has commonly been used to justify starting foreign-language instruction in schools as early as possible; but this conclusion is not in fact supported by the research. A longitudinal study of children learning English in Barcelona comparing early with late starters in English courses in schools showed that even given the larger number of total hours that younger beginners had studied, their ultimate achievement was no better than that of the older beginners (Muñoz, 2006). This result corresponds to the findings of Swain and her colleagues in extensive studies of students in immersion courses in Canada (Swain, 2000). Pause for thought What are your own feelings about starting English lessons early in schools? Comment In my country, English lessons are increasingly introduced – usually one or two a week – in the early years of primary school, while all the rest of the curriculum is taught in another language. When I ask the question on the previous page, I get a lot of different answers, depending on who is being asked. Parents, school principals, teachers of other subjects usually assume that starting English early in schools is a good thing. The majority of English teachers and other ELT professionals, on the other hand, are strongly opposed. Their opposition is not only because of the reasons given below, but also because the teachers who are leading these English lessons are usually not English teachers, but the class homeroom teachers. These may or may not be fluent in English and in any case do not have much knowledge of effective language-teaching procedures.
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".