Bibliographic record
Abstract
This article addresses the nature of historical change, focusing on experimental work presenting the motivations and mechanisms for language change. The two models of phonetic variation explaining sound change include Lindblom's H&H theory and Ohala's phonetic listener-based model. A smaller category of sound change falls under the scenario that Ohala calls hypercorrection, whereby the listener performs an unnecessary, inappropriate correction of the signal, and ends up producing a new form. Hypercorrection often results in dissimilation. Gestural reassignment captures the listener's failure to identify correctly the source of a particular property of the signal, as in Ohala's model. Gestural misparsing can also explain cases involving the apparent insertion or deletion of a gesture. The most widely cited formulated model of phonologization is by Hyman. The process involves two steps that include phonetic variation leading to phonological variation (phonologization), and phonological variation leading to distinctive variation (phonemicization). The speech recognition involves a calculation of distance in phonetic space between an auditory stimulus and the stored exemplars, and the application of a classification rule to these distances. Exemplar-based speech production involves generation of an output based on mean phonetic properties of the exemplars of the target category.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".