TOWARD A DESIGN FRAMEWORK FOR INTERNATIONAL PEER DISCUSSIONS: TAKING ADVANTAGE OF DISPARATE PERSPECTIVES ON SOCIO-SCIENTIFIC ISSUES
Bibliographic record
Abstract
This paper describes how we have adapted the WISE technology and curriculum for use in an international setting. We also report on a cross-cultural collaboration between the two authors, representing the WISE project in the U.S. and its counterpart, called Viten (see http://viten.no) in Norway. After introducing the WISE platform and describing our collaboration, we present a brief comparison of the Norwegian and U.S. educational systems. We then describe “Viten.no,” the national level program that has grown around this effort. Next, we present our designs for a collaborative activity where students from our two countries first perform a WISE (or Viten, respectively) inquiry project concerning wolf populations and biodiversity, followed by a sequence of online discussions designed to capitalize on cultural and geographic differences for purposes of conceptual learning. Finally, we describe the outcomes of our classroom trials of this international curriculum, which are limited in scale but sufficient to allow the framing of some design principles. We close with a discussion of the implications of such curriculum, and our own current efforts to continue this line of research.
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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 | 0.122 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".