Creating Transformative Spaces in Education: Facing Humanity, Facing Violence
Bibliographic record
Abstract
This paper considers the difficulties that accompany projects in education variously configured around a multicultural or intercultural label, particularly when they are built upon, for example, idealised conceptions of humanity, notions of the common good and dialogue, or ideas of recognition. My premise here is that, while such idealisations may be constructed with the best of intentions, they occlude discussions of how to face the violent realities that can also be part of social interaction. My focus here is on the existential conditions that frame our encounters with other people, and how such existential concerns lead us to confront more openly the violence that can inhere in such encounters. Beginning from this existential position, I argue, actually invites alternative ways of formulating transformative work in education, namely a focus on the present. Secondly, I turn to explore the transformative spaces created by performance artist Marina Abramovic, and how her projects reveal what we are up against existentially when it comes to facing humanity in the here and now, in all its messiness. And, finally, I make the suggestion that an education committed to the existential conditions of facing humanity can be built on a reconceptualization of conversation, as opposed to dialogue. Here I argue that conversation offers the kind of potential for transformation in its open-endedness and anarchic sensibilities.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.108 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".