Bibliographic record
Abstract
After two successful conferences (2015 & 2016) at Reutlingen University, the third Drama in Education Days was held at Konstanz University of Applied Sciences, June 30th and July 1st, 2017. The bilingual (English/German) conference focuses on best practice and research in the field of drama and theatre in education in second and foreign language teaching, and is organised by Dr. Stefanie Giebert (Konstanz University of Applied Sciences, Germany) und MA Eva Göksel (Centre for Oral Communication, University of Teacher Education Zug, Switzerland). The two-day event caters to teachers, scholars, and performers working with drama and theatre in language education at all levels – primary through to tertiary. This year’s conference attracted 45 participants from 9 countries including Austria, Canada, France, Germany, Kirgizstan, Spain, Switzerland, the US, and the UK. The conference kicked off Thursday, June 29th, with a hands-on pre-conference workshop, during which Tomáš Andrášik (Masaryk University) demonstrated how improv theatre creates a positive classroom atmosphere and fosters communication skills. In the space of two hours, workshop participants tested out techniques to lower communicative anxiety and to develop public speaking skills. Exercises aimed at building self-confidence in speaking and listening and to empower spontaneous and authentic communication were also presented. ...
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.014 |
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".