Bibliographic record
Abstract
This thesis is a case study in literary translation studies. It consists of a creative component comprising 70% of it, which is an anthology of contemporary English Instagram poetry translated into Arabic, and a critical component comprising 30%, which is a commentary outlining the linguistic, literary, and cultural aspects promoting my translation choices. Born in Punjab, India in 1992 to Indian parents, Rupi Kaur emigrated with her family as a child to Canada where she became a leading Instagram poet, illustrator, and stage performer. Kaur has received little academic attention to date and has remained undertranslated in Arabic. Instagram poetry is both very widely accessible and culturally specific. Literary translators and academics have demonstrated how deeply intertwined language, multimodality, poetry, and culture are. Translating Kaur's poetry equivalently is a complex task, both culturally and linguistically. Therefore, this thesis seeks to study the challenges of translating contemporary poetry in a multimodal context and provides both translators and scholars with a discussion of negotiating verbal and non-verbal meanings across languages and cultures. By providing an annotated translation of Milk and Honey, The Sun and her Flowers, and HomeBody in Arabic, accompanied by a critical commentary, I endeavor to show how, despite all the restrictions imposed by the field of multimodality and literary translation, as well as the difficulties of poetry translation, a translator can still produce a well thought-out and reliable translation that conveys the literary cultural and visual aspects of poetry, and more specifically of this young best-seller contemporary Instagram poet.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".