Willing the impossible: Reconciling the Holocaust and the Nakba through photograph-based storytelling
Bibliographic record
Abstract
On May 14, 1948 Israel proclaimed its independence, establishing a national home for the Jewish people following the horrors of the Holocaust. However, for Palestinians this proclamation was tied to the Nakba or catastrophe, a term used to mark their displacement, dispossession, and occupation. This cycle of violence has made ethical dialogue and the witnessing of the other’s trauma difficult. To begin bridging this divide, my dissertation takes up the impossible yet necessary task of “willing the impossible” (Butler, 2012, p. 222), which entails thinking the unequal yet bound tragedies of the Holocaust and the Nakba contrapuntally, morally and ethically engaging with alterity, and envisioning a new polity based on coexistence, justice, and equitable rights (Said, 2003). It does this by bringing Edward Said’s (2000; 1993; 1986) theories of narrative, memory, and photography, Hannah Arendt’s distinction between “fictional” and “real” stories (1998, p. 186), and Arielle Azoulay’s concept of “the civil contract of photography” (2008, p. 85) into praxis through a unique photograph-based storytelling method. First, I conducted interviews with Palestinians and Israelis living in their respective Canadian diasporas who are of the Holocaust and Nakba postmemory generations (Hirsch, 2012). During these interviews participants narrated their stories of how the Holocaust and/or the Nakba have impacted their lives using family photographs. Second, participants exchanged their stories and photographs with fellow participants from both cultures. Finally, I conducted a second round of interviews in which participants reflected on the experience of narrating their stories and photographs, engaging with the other participants’ stories and photographs, and the research process as a whole. Ultimately, my dissertation demonstrates that storytelling and photography enable the “occasions” (Fabian, 1990, p. 7) and “conditions of possibility” (Culhane, 2011, p. 258) necessary for willing the impossible through “civil imagination” (Azoulay, 2012, p. 5). That is, by narrating and exchanging their postmemories of the Holocaust and/or the Nakba through photographs, my participants were able to connect rather than compare their histories of suffering and exile, take moral, ethical, and political responsibility for one another, and imagine a new form of cohabitation grounded in justice and equitable rights for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".