Irish Setters and Palestine Retrievers: Liberal Zionism in Beckett’s <i>Watt</i> manuscripts
Bibliographic record
Abstract
Samuel Beckett wrote Watt in occupied France. Its defining theme would be complicity. From the Watt manuscripts, we can glean insights into what Beckett was thinking as he tried to work through the rise of fascism and France’s capitulation to it. Central to that attempt was an artistic dialogue – vigorously comic but also rigorously ethical – with W. B. Yeats. Watt is, among other things, an Irish Big House novel, and it revolves around the problem of disposing of a landlord’s leftovers by way of a dog – or colony of famished dogs – bred for that purpose. This has been read as a parody of liberal political economy with its founding problem of waste. In the manuscripts, the ideal breed of dog for the task is a cross between an Irish setter and a Palestine retriever. I read this detail as a critique of the biopolitics of liberal Zionism, which Beckett understands as an aspect of the biopolitics of liberal colonialism more generally. Zionism’s early forays into Palestine were already enmeshed in the same colonial dynamics that had shaped the English occupation of Ireland. Characteristically, Beckett covered his tracks as he moved towards publication. But the manuscripts, written in the white heat of occupation, trace the emergence of his ethical response, both to Yeats’s incipient fascism and the liberal pretensions of the Zionist project.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".