Language and Power Nexus: A Critical Study of Pakistani Political Discourse
Bibliographic record
Abstract
The present study presents a critical view of the speech delivered on May 09, 2011 by the prime-minister of Pakistan, Yousuf Raza Gillani. Following the language of the political discourse, this speech is delivered in the parliament house in front of the speaker, but is meant for the masses. The position of the speaker remains uniform as the questions are asked in the end alone. However, the speech is meant for both the addressee present at the time of the speech, and the assumed masses. It was found out the pronouns we, our, were constantly used to shift the responsibility on Al-Qaida whereas “I” was used for authority in order to digress the discussion from the topic. The pronouns and the vocabulary together establish the in-group or out-group category. The solidarity is shown towards the masses to get their support and defense is shown towards the allies who are accusing the government of fraud and nefarious ploy. Mystification is performed at a number of places to hide truth and claim the truth alternatively.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".