“It’s hard to speak Filipino, why is that?” a case study among non-Filipino speakers
Bibliographic record
Abstract
Environment plays an important role in developing language fluency. The environment includes the geographical location and significant people like parents, siblings, friends, and teachers. Geographically, the dialects fluently spoken at Central Philippine Adventist College are Hiligaynon, Cebuano, and English. As observed, the pupils of Central Philippine Adventist College Elementary School (CPACES) struggle with Filipino language fluency. Many cannot speak the Filipino language fluently which challenges their learning in classes using the Filipino language as a medium of instruction. This study aimed to determine the difficulties in speaking the Filipino language fluently among CPACES pupils. Purposive sampling was used. Five pupils were interviewed using validated guided questions. This study utilized a qualitative case study design. Specifically, the framework of Ranan was employed to analyze the data. The study found that the mother tongue of the CPACES non-Filipino speakers is English, and they had not been exposed to the Filipino language. This non-exposure and the absence of somebody motivating them to speak Filipino have led the participants' to hardly understand Filipino. Further, their parents did not introduce Filipino learning materials and do not speak Filipino. So, to help CPACES non-Filipino speakers to become fluent in Filipino language, parents and significant people surrounding them must intentionally use Filipino at home and school, and find ways to access Filipino reading materials, and converse with them using the Filipino 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".