Bibliographic record
Abstract
Hannah Arendt articulates natality as the very “essence of education.” Natality expresses the unique capacity of each person to bring about something new in relation to an inherited world. Education’s difficult work, in Arendt’s view, is not only to introduce students to the truths of the world as it is, but also to nurture the capacity to make this world become something new. But what are the psychic difficulties inherent in allowing subjects to become new people in the aftermath of social traumas such as gender, class and racial inequality? Arguing against educational approaches that universalize identity, I suggest that an ethos of forgiveness supports a cosmopolitan educational project, which articulates the necessity of responsibility across social difference and beyond inherited notions of group belonging. Forgiveness does not replace the demand for justice, a concern that worries many critical educators, but opens us to the interpretive work of forgings new meaning, and new forms of ethical sociality, from the site of difficulty. Forgiveness’s work in education is not to dictate a particular sanctioned position, but to generate the conditions for subjects—both perpetrators and victims of social injustices—to continue reconstituting themselves as individuals in the process of becoming. Taking up CitationClement Virgo’s feature film Poor Boy’s Game (2007), which explores the aftermath of a devastating racist incident, I suggest that cultural production, which gives narrative voice to the difficult affects of social trauma, offers a curricular model for engaging with the, at times unthinkable, possibility that life can continue after horror.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".