Bibliographic record
Abstract
In northern Europe and Scandinavia, there is a tradition called Didaktik. It can be seen as both the art of teaching and as the “science for teachers”, helping us teachers answering didactic questions about WHY?, WHAT? and HOW? to teach (and support learning). Many areas of subject-specific Didaktik have in recent decades evolved from mainly practice-based methodology to quite independent research areas. This applies, for example, to the field of science-Didaktik (i.e., Science Education). Part of this field has a special interest in educational activities for socio-ecojustice. For instance, it can include complex issues used in teaching to build bridges across different curriculum subjects, among them STEM-subjects, in support of sustainability, reflexive Bildung and socio-political activism. The focus in this paper is on socalled didactic models and modelling aiming at actions for socio-ecojustice. In particular, the paper presents a model for eco-reflexive Didaktik, an example of a didactic model. Didactic modelling is the name for the processes when didactic models are used and developed, often by researchers in collaboration with practitioners. The didactic model in focus here is based on philosophical ideas and orientations, such as holism, critical realism, egalitarianism, altruism, reconstructionism and critical pedagogy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".