Bibliographic record
Abstract
This thesis investigates the formal properties of phonological representation and computation. The starting point of the approach taken here is that these can and should be investigated independently of the effect that extraphonological factors, most notably phonetics, have on the shape of individual phonologies. In chapter 1, I summarise the conceptual and empirical arguments for a model of autonomous phonology. Then I discuss the differences between substancefree approaches to phonology, including the Concordia school (Hale & Reiss 2000a,b, 2003; Hale et al. 2007; Hale & Reiss 2008), the Toronto school (Dresher et al. 1994; Avery 1996; Dresher 1998; Avery & Rice 1989; Rice & Avery 1991; Piggott 1992; Rice 1993; Dresher 2001, 2003 inter alia), Element Theory (Harris 1990, 1994; Harris & Lindsey 1995; Harris 2005, 2006), the Parallel Structures Model (Moren 2003a,b, 2006) and radically substance-free phonology (Odden 2006, this thesis). The approach followed in this thesis is the most substance-free of the alternatives examined: neither phonological computation, nor phonological primes are innately connected to phonetic (or other extra-phonological) correlates. I discuss different formal aspects of phonological representations, and argue for a model using privative indexical features that can freely enter into feature geometrical dependency relations with one another. Finally, I summarise the most important properties of the architecture of radically substance-free phonology. Chapter 2 deals with integrating substance-free phonology and Optimality Theory (Prince & Smolensky 1993). I formalise featural identity constraints in a way that is compatible with a model using privative features and an unrestricted feature geometry. I also formalise Max and Dep constraints on features, and show how the model presented here can account for ‘fea-
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.022 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".