Exploring Language Features of Male and Female Speakers in Pakistani TEDx Talks: A Corpus-based Comparative Analysis
Bibliographic record
Abstract
The study explores the linguistic patterns in Pakistani TEDx Talks. It is based on gender-based language use. It consists of ten talks selected from YouTube and applies both quantitative analysis with the help of AntConc 3.4.4w and qualitative interpretations by researchers. Based on modern professional speaking contexts, this study examines two models including Lakoff’s Deficit Model (1975) and Tannen’s Difference Model (1990). Findings demonstrate that Lakoff’s model presents a more simplified gendered language and inadequately explain power relations in formal speech. Tannen’s model is more flexible but limited by binary gender assumptions. It fails to capture situational variation in speaking strategies. This research examines how cultural and contextual factors, along with global conditions, influence the use of the Pakistani language in TEDx Talks. Professional interaction requires speakers to use language strategically, rather than adhering to rules defined by gender norms. 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Gender-based linguistic variations in Pakistani parliamentary debates. Pakistan Journal of Language Studies, 7(1), 22–39. Zulqarnain, M., & Saeed, A. (2020). Gender stereotypes in the language of Pakistani newspapers. Journal of Gender and Social Issues, 19(1), 15–30.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".