Bibliographic record
Abstract
Alex Howard, “The Pains of Attention: Paratextual Reading in Practical Education and Castle Rackrent” (pp. 293–318) In Practical Education (1798), Maria Edgeworth and Richard Lovell Edgeworth’s treatise on rationalist pedagogy, the authors define attention as a form of painful “mental labour.” The habit of concentrating, they suggest, must be carefully cultivated before the intellectual pleasure can outweigh the “fatigue” of thinking—and to do so, “those who expect to succeed in the art of teaching” must always remember “that we can attend to but one thing at a time.” Edgeworth’s ironic annotations to Castle Rackrent (1800), however, gleefully flout these rules. By formalizing the separation between narrative and contextual material, the Editor’s footnotes diversify—and intensify—the annotated novel’s claims on its reader’s attention. This essay reframes the Editor’s paratextual interruptions as deliberate pedagogical challenges to the “lazy” adult reader’s stunted faculty of attention. Investigating the phenomenology of paratextual reading, I argue that Edgeworth’s novel aims to empower its readers to gather, to process, and to retain the information that will guide them toward more responsible political judgments and more nuanced methods of knowledge production. Ultimately, by juxtaposing the habits of pleasurable attention required of responsible intellectual laborers with the realities of labor relations on the Irish estate, Edgeworth presents the novel’s pedagogy as a necessary intervention into Anglo-Irish labor relations at the critical moment of Union.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".