Bibliographic record
Abstract
The present research deals with the identification of triphthongs in Pakistani English (PakE). A triphthong is the combination of three different vowels. There are five triphthongs in English (Roach, 2009) but those are absent in PakE due to the language variation phenomenon. As, we know that every language has a different lingual approach than the other therefore, if we find any similarity that is just a matter of chance and coincidence nothing else. So, in the current research, it is proposed that the native language, Urdu would affect standard pronunciation or RP in Pakistan. For the confirmation of this hypothesis, two experimental approaches are selected for the identification of triphthongs and their acoustic behavior in PakE. Therefore, sixty Pakistani speakers have been selected as a population of the research. Afterwards their speech has been recorded and analyzed in anechoic chamber. In the first step, the auditory approach has selected which reported varied vocalic segments by using syllable count method. In the second step, for knowing the acoustic behavior, the identified segments have been further investigated in PRAAT software. Then, data analysis and results have confirmed that Urdu influences and transforms the acoustic features of PakE. It is also confirmed that PakE has two triphthongs; (i) /ʊae/ (at word final position) and (ii) /ʊaɪ/ (at word medial position) which are not the part of RP phonetic inventory.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".