Voicing Palestinian Outrage in Rafeef Ziadah’s “We Teach Life, Sir”
Bibliographic record
Abstract
Historically, protest has found its way into cultural domains through various genres of literature. Because of its naturally persuasive power, performance poetry or spoken word poetry has been widely employed to communicate instantly and compellingly to vast audiences. Compared to printed works, it has performative dimensions that allow the delivery of the message through emotionally charged intonation, dramatic performance, and rhythm, generating as a consequence a heightened empathic response from audiences. Rafeef Ziadah, the Palestinian-Canadian poet and ardent advocate for human rights, uses performance poetry to express her outrage against what she sees as institutionalised Zionist discrimination based specifically on the grounds of religion and race, an injustice that has resulted in the expulsion of Palestinians from their lands. Drawing insights from Stef Craps’s Decolonisation of Trauma studies and Stuart Hall’s concept of “oppositional code”, the paper is an attempt to explain how Rafeef Ziadah’s poem “We Teach Life, Sir'”, serves as a powerful counter-narrative by delivering emotional and creative expressions that challenge and vehemently oppose the mainstream media’s often biassed portrayals. As a performance poem, it contributes to the “Poetry of Resistance” genre by not only expressing the poet’s strong disapproval of Israeli actions, but also by exposing the mainstream media’s biassed coverage of them. Because of the media’s narrative, and despite strenuous efforts to “fit” into society and abide by UN resolutions, the poet maintains that Palestinians receive tags like propagators of terrorism and hate. Those mainstream views expressed by a journalist at a press meeting with Ziadah led to the birth of the poem “We Teach Life, Sir”. It also echoes Nakba and Intifada poetry in its embodiment of Palestinian outrage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".