Speech Act Analysis: Pakistani Private Universities as a Case in Point
Bibliographic record
Abstract
The marketisation of higher education has received mushrooming growth in not only Pakistan but all around the globe for the last few years. Most of Pakistani universities are modifying prospectuses’ discourse to market their image and establish credibility in the competitive academic environment under the influence of marketization. The present study aimed to analyse speech acts in the educational discourse which were manifested in the recognition and reputation of universities. In the current study, the researcher has used prospectus discourse as the prime source of data to evaluate its significance in an educational environment. This study has employed a qualitative exploratory paradigm to analyze the data collected from prospectuses of Pakistani private universities. In this context, the present study has explored variations of linguistic characteristics i.e. speech acts which have been embedded in the administrative discourse to represent universities in the Pakistani academic market. The current study has attempted to employ speech acts as interpretive tools to investigate how persuasive language is used in the academic discourse of Pakistani private universities. The findings have revealed that the text of academic discourse is aligned with the commoditized practices of marketisation in the current scenario prevailing in Pakistan. The present study has also highlighted the hidden ideological perspective incorporated and displayed through speech acts in the educational discourse of Pakistani private universities.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 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".