Didactical Positions and Teacher Collaboration: Teamwork between Possibilities and Frustrations
Bibliographic record
Abstract
The context of this study is the Danish upper secondary school, which has undergone remarkable changes during the past ten years. Cross-disciplinary activities have been introduced as a teaching principle in order to create new skills for future generations in the knowledge society, while team organization among teachers has become obligatory in order to ensure collaboration regarding a new era of student learning. The reform has been widely discussed among teachers and in the public media as well. Our research shows that the majority of teachers support the idea of teamwork, but also that there are differences in teachers’ attitudes due to the diversity of interpretations of what constitutes good teaching and learning or what we call didactical values. We consider this an important discovery because it reveals that there is much resistance towards teamwork in the heterogeneous ways teachers understand their role as teacher. In this paper, we use data from a longitudinal study carried out in 2006-2009 to show that, in times of radical reforms such as the present, conflicts may be intensified exactly because of different didactical positions among teachers and between teachers and leaders in terms of how to create a viable connection between new structures and new teacher culture with the didactical values and practices that go along with them.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".