Bibliographic record
Abstract
The concept of compound is an interesting phenomenon in morphology. Compounds provide motivation for assigning internal structure to words. This is achieved through a process known as compounding. Compounding deals with word structure rules rather than word formation rules. Our focus in this paper is to analyze the phonological constituent processes necessary in Anaang productive words. Data was collected through structured interview using an English word list of 50 compound words. This was administered to fifteen native speakers of Anaang purposely selected. They provided the Anaang equivalence of the word list verbally, which was recorded with a tape recorder. Relevant data was elucidated from the tape, transcribed and used for analysis. Analysis shows that compounding involves the combination of stems from the lexicon into a phrase or word. Anaang compounds are made up of two elements without any further dependency holding between them. This paper therefore assets that, though compounding is a morph-syntactic process, it has implications on the structure of Anaang phonology in the sense that compounding equally involves certain phonological processes which of course affect the internal structure of the syllable. This work is a contribution to the existing phonological theories, on phonology-morphology interface.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".