Interactions : Explorations of Good Practice in Educational Work with Video Testimonies of Victims of National Socialism
Bibliographic record
Abstract
According to estimates, there are several hundreds of thousands video testimonies with victims of National Socialism. Many of the interview archives are easily accessed, including some that are available in the Internet for free. While teachers are hesitant in making use of this treasure of source materials, learners are familiar with the figure of the eyewitness as communicated via film and television. But what can be taught with the help of what in cinematographic terms is often criticised as “talking heads”? What constitutes a good learning setting? And how do users interact with the – usually digitised – video testimonies and the collections that are often available online? In January 2017 experienced educators and researchers attended an international workshop on “Localisation of video testimonies with victimsof National Socialism in educational programmes” and discussed the question of what is good practice in this specific form of educational work. This volume is the result ofthat process. It provides an insight into the conceptual and practical ideas on which the various programmes are based. The book also has a focus on video testimonies presented at historical exhibitions and includes contributions from many countries, such as Belarus,Canada, Israel, Macedonia, the Netherlands and South Africa.
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.027 | 0.058 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".