Following One's Nose in Reading W. G. Sebald Allegorically: <i>Currere</i> and Invisible Subjects
Bibliographic record
Abstract
Abstract In education, we are concerned with the teaching and learning of subjects, but the word “subject” can refer to the discipline being studied as well as the individual who is studying. In this essay, Teresa Strong‐Wilson explores this “double entendre” (which William Pinar refers to as the “double consciousness”) of curriculum studies through the analogy afforded by German author‐in‐exile W. G. Sebald's working through of difficult subjects by way of semi‐autobiographical writing that takes the form of an “invisible subject”: a preoccupation with an unnamed injustice entangled with his own upbringing. Curriculum theory, as currere, has foregrounded the autobiographical. While the place of autobiography in curriculum studies has often been taken to mean writing (especially of a confessional sort), currere is more an allegorical method of study, of intellectual engagement, of learning through reading and writing, and of teaching so as to open spaces for agency. Strong‐Wilson suggests that Sebald can provide a strong example for us in curriculum studies of how to ethically bring into being an allegorical, autobiographical practice focused on “invisible” subjects of deep concern.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".