Orthographic Nativization of Hispanic Content Words in Waray Visayan
Bibliographic record
Abstract
This linguistic study investigates the orthographic nativization of Hispanic borrowed words in Waray Visayan written discourse. The textual source is a news article from Isumat News Portal written in Waray Visayan titled COVID Laboratory ha EVRMC magtitikang na an operasyon. Through a descriptive research method, the words are lexically analyzed as to content words, English equivalents, root words, Spanish equivalents, native affixes, and nativization processes. The analysis reveals that the Hispanic nouns, verbs, and adjectives borrowed by the Waray Visayan article are nativized through full lexical adoption, changing or replacement of letters, orthographic mutation, and adding of native prefixes and suffixes. Thus, the Spanish loanwords have been Filipinized in the Waray linguistic community where sociocultural, economic, and religious activities exist. This further shows that the Spanish words have found unique structure and expression in the Waray Visayan written discourse, not only in literary narratives like the study of Quebec (2021), but also in non-fiction, news article in particular. In effect, it is recommended to conduct the same study with a bigger number of corpora in both written and spoken discourses in Waray Visayan and across the Philippine languages to explore the reach of Hispanic word borrowing and nativization and to investigate other linguistic changes as they undergo nativization. Keywords: Lexical analysis; word borrowing; nativization; Hispanic words; Waray Visayan 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".