NEUTRALITY IN EDUCATION: CHALLENGING THE CALL FOR TEACHER’S NEUTRALITY WITH THE CONCEPT OF JUDGEMENT
Bibliographic record
Abstract
Starting point of this paper is the online portal Neutral Teachers, which was launched by the far-right party Alternative for Germany (AfD) to enlighten students, parents, and teachers about the principle of neutrality that civil servants have to follow. As most teachers in Germany are civil servants, the principle of neutrality applies to their positions. In a first step, the German discussion around teacher’s neutrality will be analysed with a focus on the debate about the Beutelsbach Consensus (1977), an agreement between teachers and researchers, which still has a lot of influence on how the German debate around teacher’s neutrality is conducted. This consensus is also used by the authors of Neutral Teacher to legitimate their website. In a second step, the discussion around teacher’s neutrality in the U.S.A. and Canada will be considered with reference to two different positions, which are nearly 20 years apart and illustrate an interesting shift in the debate. In the third step, Hannah Arendt’s conception of judgement will be explained with a special focus on the general standpoint and the enlarged mentality because these concepts offer a significant alternative to the call for teacher’s neutrality. The importance of judgement and its value for the teacher’s profession and a thing-centred teacher education will be outlined further in the conclusion.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.099 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".