Becoming ethically preoccupied through Currere: W.G. Sebald, Franz Kafka and narrative self-representation
Bibliographic record
Abstract
Within education, we can learn a great deal from others’ uses of narrative as a site of praxis from which to work through difficult psychic processes. The narratives published by W.G. Sebald and Franz Kafka—as well as what we know about these authors’ narrative processes—hold important insights for the kind of narrative writing that can happen in currere. Born in Germany near the end of WWII and inheriting the heavy burden of the Holocaust, Sebald was concerned with the social implications of writing as a form of witness, even as he was persuaded that a narrative approach was more powerful than discursive prose. Sebald saw in the writing of narrative an attempt at restitution. For Franz Kafka, the writing of literary texts offered the only space in which he experienced some redemption from (as he called it) “murderers’ row.” Despite their stature, both considered themselves as periphereal writers; writing came from a felt sense of precarity and vulnerability. Both relied on unreliable narrators. By exploring the relations between Sebald’s and Kafka’s writing lives and their melancholy, I inquire into how both were driven by a sense of urgency in writing narratively (one form of which is literature) and look at how such writing embodied an ethical probing of unsettling preoccupations, in ways of compelling interest to projects of subjective/social reconstruction.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".