Stress Placement in English Quadri-Syllabic and Five-Syllabic Suffixed Words and Their Roots by Pashto Speakers in Khyber Pakhtunkhwa of Pakistan
Bibliographic record
Abstract
This research study investigates the pattern of English (primary) word stress in quadri-syllabic and five-syllabic suffixed words and their roots by Pashto speakers in Khyber Pakhtunkhwa of Pakistan and the effect of suffixation on stress placements. These suffixes in English language are called shifters which shift strong stress to the antepenultimate (third from the last), penultimate (second from the last), and ultimately (last) syllables, as well as those suffixes that do not shift strong stress to other syllables. The data was collected from sixteen Pashto language native speakers in Khyber Pakhtunkhwa Pakistan, by way of recording their oral-reading of a card that contained the selected words. The findings of this study indicate that primary stress pattern varies among quadri-syllabic, and five-syllabic, suffixed words. The three types of suffixes in English language assert different degrees of effect on subjects stress placement, which can influence the amount of correct productions by the subjects. Actually, the suffixes “cial” or “tial” and “ic” state a great effect on subjects primary stress placement, because the subjects were capable of generating the shift in primary stress in penultimate syllable. Unlike the greater number of incorrect productions in “tory” and “ity” suffixed words, the subjects were sensitive to the change of stress pattern, which assists a great number of correct productions in “cial” or “tial” and “ic” suffixed words. The findings disclose the fact that there was extreme unawareness of the strong stress shifting effect by Pashto speakers in Khyber Pakhtunkhwa, which further needed more attention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".