Lexical representation and processing of word-initial morphological alternations: Scottish Gaelic mutation
Bibliographic record
Abstract
When hearing speech, listeners begin recognizing words before reaching the end of the word. Therefore, early sounds impact spoken word recognition before sounds later in the word. In languages like English, most morphophonological alternations affect the ends of words, but in some languages, morphophonology can alter the early sounds of a word. Scottish Gaelic, an endangered language, has a pattern of ‘initial consonant mutation’ that changes initial consonants: Pòg ‘kiss’ begins with [ph], but phòg ‘kissed’ begins with [f]. This raises questions both of how listeners process words that might begin with a mutated consonant during spoken word recognition, and how listeners relate the mutated and unmutated forms to each other in the lexicon. We present three experiments to investigate these questions. A priming experiment shows that native speakers link the mutated and unmutated forms in the lexicon. A gating experiment shows that Gaelic listeners usually do not consider mutated forms as candidates during lexical recognition until there is enough evidence to force that interpretation. However, a phonetic identification experiment confirms that listeners can identify the mutated sounds correctly. Together, these experiments contribute to our understanding of how speakers represent and process a language with morphophonological alternations at word onset.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".