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Record W4367723283 · doi:10.37058/jelita.v2i1.6435

Orthographic Nativization of Hispanic Content Words in Waray Visayan

2023· article· en· W4367723283 on OpenAlexaboutno aff
Robertgie Laprodes Piañar

Bibliographic record

VenueJELITA Journal of Education Language Innovation and Applied Linguistics · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsRoot (linguistics)NounPrefixNarrativeHistoryPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.281
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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