Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Vietnamese is spoken by over 89 million people in Vietnam and it is one of the most commonly spoken languages other than English in the US, Canada and Australia. This study defines between one and nine different dialects of Vietnamese spoken in Vietnam. In Vietnamese schools, children learn Standard Vietnamese which is based on the northern dialect; however, if they live in other regions they may speak a different dialect at home. METHOD: This paper describes the differences between the consonants, semivowels, vowels, diphthongs and tones for four dialects: Standard, northern, central and southern Vietnamese. RESULT: The number and type of initial consonants differs per dialect (i.e. Standard = 23, northern = 20, central = 23, southern = 21). For example, the letter "r" is pronounced in the Standard and central dialects as the retroflex /ʐ/, northern dialect as the voiced alveolar fricative /z/ or the trilled /r/ and in the southern dialect as the voiced velar fricative /ɣ/. Additionally, the letter "v" is pronounced in the Standard, northern and central dialects as the voiced bilabial fricative /v/, the southern dialect as the voiced palatal approximant /j/ and in the lower northern dialect (Ninh Binh) as the voiceless bilabial fricative /f/. Similarly, the number of final consonants differs per dialect (i.e. Standard = 6, northern = 10, central = 10, southern = 8). Finally, the number and type of tones differs per dialect (i.e. Standard = 6, northern = 6, central = 5, southern = 5). CONCLUSION: Understanding differences between Vietnamese dialects is important so that speech-language pathologists and educators provide appropriate services to people who speak Vietnamese.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it