Categorical and non-categorical perception of marginal phonemes
Bibliographic record
Abstract
Marginal phonemes and contrasts occupy a complex position in linguistic theory, as traditional theories of phonemehood do not account for marginality. However, contemporary linguistics has found that phonemic contrast strength is not fixed in childhood but rather continues to change, thus implicating the lexicon, the set of words a speaker knows, as a factor in the behavior of phonemic contrasts.This dissertation takes this link between category strength and the lexicon and treats it as an empirical question. I identify token frequency and type informativity, measures of frequency and predictability within a lexicon, as potential predictors of individual behavior. I then justify and present an experimental procedure for an eye tracking, two-alternative forced choice, categorization study on three phonetic continua — [a͡ɪ]-[ʌ͡i], a marginal contrast; [a͡ɪ]-[ɔ͡ɪ], a classic phonemic contrast; and [ʌ͡i]-[ɔ͡ɪ], a mixed case — in Canadian English, using the visual world paradigm. I discuss decisions that were made in the design of the experiment, including how individual lexicons were probed and why multiple continua were examined.\nAnalyzing the resultant eye tracking data both graphically and by GAMM model comparison, I find that behavior was not interpretably predicted by my selected predictors, though their contribution to bias, a normalized preference measure, was statistically significant. I report on the behavioral patterns that were found in the categorization data and show that participants with differential behavior did not have statistically significant differences in either frequency or informativity in nearly all cases.\nMy findings come as a surprise, as predicting that variation in the lexicon (operationalized as frequency and informativity) should influence linguistic behavior is both obvious and supported by the literature. I thus present my thoughts on why these predictors were not significant ones as well as my suspicion that the process of calculating these lexical statistics was poisoned by the likely incorrect assumption that a marginal phoneme can be treated as if it were a strong phoneme for the purposes of calculation. I close with suggestions for future work that could advance understanding of this issue, including potential test cases and the need for alternative operationalizations of frequency and predictability for marginal phonemes.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".