Phonological familiarity and short-term verbal memory: implications for teaching English as a foreign language (TEFL)
Bibliographic record
Abstract
This paper describes a small-scale experiment in speech perception and shortterm memory, the results of which suggest some shortcomings in the conventional pedagogy of teaching English as a foreign language (TEFL). The experiment used a common practice of speech perception research, that of measuring perception of ‘nonwords’ that are fabricated words that obey the phonology of a language, but are contrived to be words without meaning. Only one of the respondents was multilingual, and all of them had lived in English Canada since childhood and considered English as their native language. They were asked to listen to ten two-second utterances and repeat them as accurately as possible immediately after hearing them. Five of the utterances were in the African language, Hausa, which none of the volunteers had any familiarity with. The other five utterances were strings of nonword English. The experiment tested the hypothesis that the volunteers would be able to repeat the familiar English-like utterances more accurately than utterances from a language with an unfamiliar phonology. All the volunteers reported that the Englishlike utterances were easier to hold in memory and repeat, and the experimenter’s 社会イノベーション研究 2007年10月16日掲載承認 第3巻第1号(91-112) 2008年1月
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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 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".