Unveiling Self-Identity of Teachers: A Critical Discourse Analysis of Perception and Interpretation of Selected Poems of Rupi Kaur’s “Homebody”
Bibliographic record
Abstract
This study focuses on the unique amalgamation of linguistic and visual elements in Rupi Kaur’s Instapoetry, and how it contributes to the self-identity representation of university-level teachers in English Literature or Linguistics. Ten participants, each with considerable teaching experience and active research engagement in their respective fields were individually invited to participate in this research from various universities in Pakistan. Data collection includes two sessions of semi-structured interviews. Additionally, a supplementary questionnaire was used to gather demographic information and insights into participants’ academic backgrounds. The transcribed data were analyzed using Norman Fairclough’s Three-dimensional framework of CDA, revealing the transformative role that linguistic and visual elements play in shaping teachers’ self-identity by fostering their innovation, individuality, and inclusivity as professionals and individuals. However, the study also highlights persistent challenges related to cultural conservatism and academic freedom when adopting new trends in Instapoetry. The study suggests practical implications for teachers and educational institutions to benefit from a subtle understanding of the relationship between Instapoetry and self-identity. The study has two main limitations: a small sample size of university-level teachers and a primary focus on selected poems from Rupi Kaur’s “homebody” for analysis.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".