Learning from the Past, Teaching for the Future: A Forum
Bibliographic record
Abstract
The Mark and Gail Appel Program in Holocaust and Antiracism Education: “Learning from the Past, Teaching for the Future” (TFTF) is a tri-national program that brings students from Canada, Germany, and Poland to sites of Holocaust memory to focus on the history, experience, representation, and memorialization of the Holocaust and its implications for other instances of atrocity, racism, and genocide. This forum gathers five voices from the 2013-2014 cohort of TFTF in order to reflect, a decade after the program, on the program’s challenges, opportunities, and lasting impacts. The forum documents the various ways TFTF has influenced the lives and career trajectories of each of the forum participants. The forum is bookended by reflections from its editors—a program organizer and a program participant—on the potentials of experiential learning, the promise of an international network of educators, and the processing that a program like TFTF continues to demand.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".