Bibliographic record
Abstract
This study aimed to produce a formalism of word stress in Qassimi Arabic (QA), which is a sub-dialect of Najdi Arabic (NA), using a constraint-based approach. To this end, this paper investigated two main topics: The first topic explored word stress in QA. Word stress in QA, as well as in NA, is predictable; it can be determined by syllable weight and position. However, two cases do not conform to such straightforward stress rules. These cases are represented by the words: [ʔal.ʕa.sˤir] ‘afternoon’ and [ʔa.ʕa.rif] ‘I know’. Derivational analysis of these exceptions shows the importance of relating the surface structures of such forms to their underlying representations. The second topic aimed to make a formalism for stress patterns in QA using optimality theory (OT). Thus, QA word stress rules and their exceptions are translated into conflicting constraints that are ranked relative to one another by the use of constraint-relation tableaux. This ranking eventually produced the following constraint-relation hierarchy: Lx≈Pr, SYLLABLE-INTEGRITY, TROCHAIC, FAITH-PK >> NONFINAL >> *[ʔa. >> FTBIN-µ, WSP, ALL-FEET-RIGHT >> MAIN-RIGHT, PARSE-σ.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".