An Analysis of Chat Abbreviations and Slangs of the Students of the University of Port Harcourt
Bibliographic record
Abstract
This paper examines chat abbreviation and slangs of the University of Port Harcourt students. The qualitative research design was used in analyzing the data collected using the descriptive content analysis. The sampling technique adopted in this paper is the purposive sampling technique and source of data for this paper is primarily observations from students of University of Port Harcourt online platforms and social media used by students and interview. The findings of this paper reveal that students of the University of Port Harcourt use single letters, digits and a combination of both to replace words while some of the words are shortened, contracted and clipped. The paper also establishes that the students abbreviate words in order to minimize cost, save time and add style to their writing. The paper further establishes that chat abbreviation and slangs of the University of Port Harcourt students do not follow the rules of grammar of English language since they are composed haphazardly. It was observed that students of University of Port Harcourt do not use their punctuation marks properly in their formal writings. This paper also reveals that chat abbreviations and slangs have affected the proper usage of English language by some of the students of the University of Port Harcourt. This paper recommends that students should become more conscious of the dangers of chat abbreviation in their academics and identify as well as appreciate the appropriate setting in which to use them.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".