Shared Orthography: Do Shared Written Symbols Influence the Perception of L2 Sounds?
Bibliographic record
Abstract
This research investigates whether English speakers who learn Mandarin Chinese via a familiar orthography differ from those who learn via a non‐familiar orthography in their perception of English–Mandarin sound pairs. Canadian English speakers (n= 32) participated in a series of experimental tasks. The tasks included pre‐ and posttest perception tests and language classes where the participants learned Mandarin through 1 of 3 means: Pinyin, the familiar orthography; Zhuyin, the non‐familiar orthography; or no orthography. The results indicate that the 3 learning groups exhibited similar perceptual performances. These results are discussed in terms of the strength of the established first language (L1) orthographic system, the cognitive load, and the length of time required for the development of new symbol–sound associations. The data suggest that Mandarin instruction via Zhuyin does not appear to have an advantage over instruction via Pinyin, as conflict between 2 orthographic systems appears to neutralize any potential benefits. This is the first systematic study to investigate the potential influence of the L1 orthographic code on second language speech perception.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".