Sound-Spelling Correspondences in FL Instruction: Same Script, Different Rules
Bibliographic record
Abstract
Auditory perceptual and orthographic confusions challenge foreign language (FL)learners. Hearing first-language (L1) learners establish reliable acoustic parameters for sound categories during infancy (Strange, 2011; Werker & Tees, 1984), before learning how to encode them orthographically. In contrast,FL classrooms simultaneously expose adult learners to new second language (L2)sounds and new orthography, a process which is fundamentally different from L1alphabetic literacy. Even if both employ the “same” script (e.g., Roman alphabet), grapheme-phoneme correspondences (GPCs) are not congruent between languages, and languages differ in internal consistency of GPCs.Perceptual categories for FL are not robust, requiring greater attentional resources to distinguish L2 phonetic contrasts (Strange, 2011), and likely influenced by the L1, and learners’ GPCs are influenced by the L1 (or priorL2s), especially when languages share a script (e.g., German, English). Interaction between orthography and acquisition of L2 sound categories is widely acknowledged, yet poorly understood. We review L2 segment perception research, alphabetic literacy, and early-stage FL instruction, then present results from a longitudinal study of 19 adult FL students beginning to learn German. Prior to instruction, participants spelled 92 auditorily-presented German words featuring 19 phones (9 consonants, 10 vowels). After one semester, they spelled92 words from course vocabulary lists and 92 unfamiliar words with the same GPCs. We analyze spelling responses to characterize GPC development in FL and generalizability of early gains to novel words.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".