Beckett's Dying Remains: The Process of Playwriting in the <i>Ohio Impromptu </i>Manuscripts
Bibliographic record
Abstract
Samuel Beckett's works originate as highly personal accounts of the author's life and gradually evolve into elusive, enigmatic texts. In Beckett's oeuvre, autobiography provides the foundation for writing that then undergoes a painstaking process that purges the text of the author's identity. It is as though Beckett records aspects of his past with the conscious intent of undoing them, paradoxically developing his texts out of the erasure of autobiography. Far from creating works of "cryptobiography," that is, works in which the author only remotely disguises his identity, Beckett aims "not so much to disguise autobiography as to displace and discount it". The progression of Beckett's manuscripts exposes this refusal to accept the very autobiography that spawns his creative material. In particular, the manuscripts of the late play Ohio Impromptu (1981), including the many unpublished false starts now housed at Reading University's Samuel Beckett Collection, exemplify Beckett's attempt to unwrite himself through what he once called his "literature of the unword". This essay focuses on these recently acquired manuscripts to illustrate how Beckett's unique process of self-erasure informs his final text. In order to elucidate this process before turning to the specific manuscripts, I introduce the concept of "derangement" and its contribution to Beckett's dying presence in his works.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".