Speculations on qualities of difficult knowledge in teaching and learning: an experiment in psychoanalytic research
Bibliographic record
Abstract
This paper explores two questions in relation to the authors' project, “Difficult Knowledge in Teaching and Learning: A Psychoanalytic Inquiry.” They describe how their original question, “What makes knowledge difficult?,” transformed into “What is it to represent ‘difficult’ knowledge?” They speculate on the resonances that this crisis of representation leaves in narration by way of three psychoanalytic concepts: deferred action, transference, and symbolization. They consider constructions of difficulties in teaching and learning from the vantage of psychoanalytic writing and their own attempts to interview university teachers and students on how they think about difficult knowledge. They offer a conceptual archeology of their project that highlights the shift from the first to the second research question, some clinical discussion on the difficulties of narrating teaching and learning, some constructions of difficulty proposed in their research protocol, and constructions of difficulty in their interviews. They conclude by discussing how the very design of their research enacted the crisis of representing teaching and learning.
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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 | 0.046 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".