Neither Deep nor Shallow: A Classroom Experiment Testing the Orthographic Depth of Tone Marking in Kabiye (Togo)
Bibliographic record
Abstract
The experiment reported here tests the Lexical Orthography Hypothesis, that is, the notion that the output of the lexical phonology is the most promising phonological depth for an exhaustive representation of tone by means of diacritics in the orthography of atone language. We conducted a controlled classroom experiment with 97 secondary school pupils learning written Kabiye, a Gur language of northern Togo. After testing their baseline skills in writing the standard orthography, the pupils participated in an eleven-hour transition course spread over three weeks in four parallel groups: DEEP (an experimental orthography representing the input of the lexical phonology), LEXICAL (representing the output of the lexical phonology), PHONEMIC (representing a level between the output of the lexical phonology and the output of the post-lexical phonology), and a control group. On the final day of the experiment, we tested their acquired skills in a dictation exercise. The results show that the LEXICAL group outperforms the other groups in three of the error types associated with adding diacritics, although they performed less well on the error type associated with writing long vowels. This initial evidence supporting the Lexical Orthography Hypothesis needs confirmation with reading and writing experiments on a variety of other tone languages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".