Bibliographic record
Abstract
Lexical innovation occurs in a language because of the inability of a child or an adult to recall conventional words for the expression of ideas or as a result of an attempt to invent new words to fill in existing gaps in a language. Loanwords constitute the most common ground for lexical innovation in a language. They are known as innovations which cannot be accounted for in terms of inheritance, and, which share a resemblance with the lexical items of the donor language. Loanwords are said to occur in a language as a result of language contact, leading to lexical enrichment. The contact between Anaang and the English language and culture have created room for the adaptation of English lexical items into Anaang with some forms of innovations/alterations. The study examined the phonological implications of lexical innovations in Anaang-English loan items. The objective of this paper is to describe the structure of the loan items, using a phonological descriptive model and to examine the effects of these innovations on the structure of the affected language. Several diverse phonological processes were applied in the modification of the English loan items to comply with the Anaang phonotactics. Anaang words were said to be closely tied to the internal structure of the syllable and severely guided by the Anaang phonotactics. Therefore, the combination of segments into words was equally constrained by the Anaang phonotactics. This phonotactics further extended to govern the distribution of segments in lexical constructions. Certain segments were restricted to specific environments; therefore loanwords were modified to comply with the phonotactics of the language. Phonotactics therefore played a vital role in defining Anaang well-formed words. This paper is relevance for the understanding of the aspects of word formation processes in language.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".