Bibliographic record
Abstract
This article offers an auto-theoretical, exploratory account of teaching writing as a Palestinian educator at the American University of Beirut in 2023–2024 during the war on Gaza and the escalation of violence in Lebanon. Written in a series of chronologically organized vignettes, the manuscript blends memoir, correspondence, lesson plans, and critical pedagogy to document how war enters the classroom through microaggressions, silencing, and institutional failure to recognize politically situated harm. Drawing inspiration from the essay tradition the article foregrounds process, uncertainty, and affect as legitimate forms of scholarly inquiry. Reflections on formative teachers in Canada, a Jewish-Canadian childhood friendship, and intergenerational memory illuminate how race, identity, and worldliness shape pedagogical becoming. The article situates experiential teaching during genocide within composition studies, arguing for the exploratory essay as an ethical and pedagogical response to crisis. It further details curricular revisions undertaken during wartime, including collaborative assessment, anti-thesis essay structures, and the use of fairytales—particularly Palestinian children’s literature—as a means of restoring imagination, solidarity, and care. Ultimately, the article testifies to the necessity of courageous dialogue, institutional accountability, and pedagogies that resist silence while affirming relational hope amid devastation.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".