“My chief pleasure has been books”: On Teaching <i>Letters of the Late Ignatius Sancho</i>
Bibliographic record
Abstract
In the Letters of the Late Ignatius Sancho (1781–82), Sancho’s erudition, wit, pleasure in the arts, and love for his wife far outweigh extended considerations of systemic racism and personal struggle. This essay discusses two classroom experiences, one in the state of Maryland (US) and the other in the UK. What we have realized in our pedagogical approaches to teaching Sancho’s Letters is how immensely entrenched the association of Black subjectivity with historical suffering remains within academia. We propose some strategies developed in our own respective teaching to help students be more alert to the complex subjectivities Sancho inhabits, including foregrounding form and composition, exploring eighteenth-century inter-textuality, and moving away from the implicit promise that a literary survey course models anything like a comprehensive or totalizing account of the period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".