Bibliographic record
Abstract
In his analysis of /dˤ/-variation in Saudi Arabian newscasting, Al-Tamimi (2020) finds unpredicatble variability between the standard variant [dˤ] and the non-standard variant [ðˤ] in different in-words positions, in different phonetic environments, and in semantically ‘content’ and suprasegmentally ‘stressed’ lexical itmes assumed to favor the standard variant. He even finds in many of these lexical items an unusual realizational flucatuation between the two variants. The present exploratory and ‘theory-testing’ study aims to find a reasonable account for these findings through examining the explanatory adequacy of a number of available phonological theories, notions, models and proposals that have made different attempts to accommodate variation, and this includes Coexistent Phonemic Systems, Standard Generative Phonology, Lexical Diffusion, Variable Rules, Poly-Lectal Grammar, Articulatory Phonology, different versions of the Optimality Theory, in addition to the Multiple-Trace-Model, as represented by Al-Tamimi’s (2005) Multiple-Trace-Based Proposal. The study reveals the strengths and weaknesses of these theories in embracing the variability in the data, and concludes that the Multiple-Trace-Based Proposal can relatively offer the best insight as its allows variation to be directly encoded in the underlying representations of lexical items, a status strictly prohibited by the rest of the theories that adopt invariant lexical representations in consonance with the ‘Homogeneity Doctrine’.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".