Review: Hanns-Fred Rathenow & Norbert H. Weber (Eds.) (2005). Nationalsozialismus und Holocaust. Historisch-politisches Lernen in der Lehrerbildung [National Socialism and Holocaust: Historical and Political Learning in Teacher Training]
Bibliographic record
Abstract
The book includes a number of essays dealing with the importance of learning to remember National Socialism (Nazism) and the Holocaust, both inside and outside the classroom. It is divided into four areas: (a) education after Auschwitz—rapprochement; (b) teacher-training at university and at school—concepts and prior experience; (c) advanced teacher-training—prior experience and reflections; (d) rapprochement worldwide. The essays range from theoretical contemplations to reflections on actual experiences, from empirical-qualitative research to concrete implementation in the classroom. Those in the last section look at how the topic of the Holocaust is being treated in Israel, Palestine, Poland and the Czech Republic. "Holocaust education" in England and the USA is also introduced together with a trilateral university project in Canada, Germany and Poland. All in all, this multi-faceted volume presents the reader with various (i.e. empirical, hermeneutic and didactic) approaches to the topic and how it is being dealt with in the school curriculum and in the pedagogy of erecting memorials. URN: urn:nbn:de:0114-fqs0801295
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".