Fighting the plague: “Difficult” knowledge as sirens’ song in teacher education
Bibliographic record
Abstract
Of the many plagues that affect communities today, a particularly insidious one is indifference and depersonalization. This plague has been articulated by Albert Camus and then taken up in an educational context by Maxine Greene. In this article we, the authors, respond to Greene’s call to co-compose curricula with our students to fight this plague. Recognizing the role of difficult knowledge as well as conscious and unconscious defenses, we develop an approach to “diversity” harmonious with radical love during these troubled times of conflict and increased visibility of hatred. Through a weaving of our experiential, embodied knowledge with theory, we consider how we might invite students to consider contemporary, historical, and ongoing inequity and structural violence. Like Sirens luring sailors to precarious shores, we seek to entice teachers and students to the difficult knowledge they might otherwise avoid as all of us together consider our ethical responsibilities to each other.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".