Thinking Clearly About Confusion: Threshold Concepts, Bafflement, and Meaning as “Contestation” in the English Classroom
Bibliographic record
Abstract
A fundamental question at the heart of literary studies concerns the intangible—and unanswerable—question of what it means to be human. To pursue this question rigorously, literary studies has deployed methods from a range of disciplines in the humanities and social sciences; while interdisciplinary approaches to English have generated a wealth of important theoretical and “real-world” interventions crucial to the discipline’s ongoing development, we risk diminishing the ineffability that lies at the heart of critical inquiry. The reasons behind this disconnect are too expansive and complex to discuss here (cf. Day, 2007; Griffin, 2005), but this workshop proceeds from the premise that it is precisely by remaining open to uncertainty, contingency, and complexity that humanities research maintains its purchase; while confusion is intuitively thought of as a problem to be avoided in the classroom, I posit that it is vital to developing mastery of difficult concepts in English Literary Studies. Through a sustained engagement with Meyer & Land’s (2005) development of threshold concepts, this workshop deploys a short lecture, large group discussions, and individual and small group activities to invite participants to investigate “confusion” as a productive pedagogical tool under the aegis of threshold concepts.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.090 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".