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Record W7066847999

Interactions : Explorations of Good Practice in Educational Work with Video Testimonies of Victims of National Socialism

2018· other· en· W7066847999 on OpenAlexaboutno aff

Bibliographic record

VenuePublication Server of the Catholic University Eichstätt-Ingolstadt (Catholic University of Eichstätt-Ingolstadt) · 2018
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTreasureExhibitionGood practiceThe InternetForm of the GoodPresentation (obstetrics)Nazism
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.236
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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