Scholasticide in Gaza and Palestine as a Portal: A Duoethnography on Silence, Silencing and the Struggle for a Better World
Bibliographic record
Abstract
The unprecedented destruction of the education sector in Gaza since October 2023, including the systematic destruction of all higher education institutions, is known as scholasticide. This has been part of the onslaught intended to impede the survival and existence of Palestinians as a people. The genocide and scholasticide in Gaza have been a global affair, from international support for Israel, on one side, to a growing global movement against the genocide, much of it spearheaded by university students, on the other. The repression and silencing of students, academics and staff who speak out against the genocide have, too, been a global affair, as has been the silence of many leaders, administrators and individuals in the global higher education sector. In this paper, we employ duoethnography as a research method and draw on our personal and professional experiences as researchers and practitioners in higher education and internationalization to critically engage with this moment and what it represents. We unpack how the events in Gaza and Palestine should influence global higher education to engage more critically with the struggles for social justice. We discuss the global responsibility during a genocide, and the responses to scholasticide in Gaza in the higher education sector. We explore what this moment means for higher education going forward, framing this around the need to organize more and better globally to challenge and dismantle coloniality, capitalism and neoliberalism which continue to wreck the lives of billions of people around the world.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".