Crafting interdisciplinary in an M.Sc. programme in management of natural resources and sustainable agriculture
Bibliographic record
Abstract
This paper discusses challenges of an educational program, where interdisciplinarity is an important ambition. A theoretical perspective on interdisciplinarity must be more than adding insights from different disciplines as surprisingly many actors still take it to be. Interdisciplinarity is a fruitful meeting-ground and constitute processes for translation and integration of disciplinary perspectives. Interdisciplinary candidates must learn and should develop skills to identify, select, translate and integrate knowledge from different disciplines into a coherent framework. From theories in interdisciplinarity, one should develop explicit theories for interdisciplinarity. A common field focus can motivate integration of and translation between disciplines. The multipurpose re-orientation in forestry as an example of natural resource management displays the need for development of management proficiency not only related to multipurpose management, but also to handle social issues and interactions between conflicting actors. Within forestry, interdisciplinary challenges are often met through implicit, tacit and experience-based "common sense" knowledge. An explicit focus on integration of and translation between disciplines as well as development of experience-based skills is required to build interdisciplinary proficiency. This includes using practical field assignments and problem-based learning approaches to develop candidates' abilities to select, translate and integrate knowledge. Key words: interdisciplinarity, environment and development, cross-epistemic communication, natural resource management and education
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".