Time and Phonology: Precedence-Based Representations
Bibliographic record
Abstract
A major factor hindering the establishment of a successful neuroscience of phonology centers around the biological viability of a given phonological framework. The ultimate aim of this project is to find potential alignments between linguistics and neuroscience. In this vein, the main topic of the thesis rests upon establishing the minimal complexity requirements for a phonological representation that is biologically plausible, cognitively sound, and empirically motivated. Heeding Minimalist proposals (Chomsky, 1995) that encourage efficiency in computation and economy in representation, I embark on an in-depth exploration of the parameters of cognition that are necessary and sufficient in a phonological representation while discounting the processes and parameters that can be said to be “domain-general”. To that end, I take seriously Ernst Po ̈ppel’s (2004) exhortation to consider the role of temporal events like linear order and precedence in the study of cognitive systems like phonology by surveying the literature on time perception. The conclusions support a separation of order from phonological representations, extending the scope of substance-freeness (Hale and Reiss, 2000) by characterizing order as substance. Such an approach can contribute to thoroughly defining the object of study and offer insight that narrows the search space for potential bridges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".