Conceptual Change Among Adolescents Using Computer Networks and Peer Collaboration in Studying International Political Issues
Bibliographic record
Abstract
The purpose of this chapter is to describe a learning environment that employs networked computers (with software similar to a sophisticated electronic mail system), and the nature of peer-group collaboration in this environment. The purpose of the technology is to facilitate communication about political, economic, and social issues among secondary and university students. The aspects of the learning environment and prototypes of the networking software, which are the basis of the International Communications and Negotiations Project (ICONS), were developed by a political scientist in the late 1970s, when such uses of computer technology were rare. The original purpose was to enhance the experience of advanced undergraduate students specializing in international relations at the University of Maryland. In the intervening years, the project has expanded and been adapted for three settings: first, in two-to six-week summer centers for adolescents from both gifted and less highly achieving populations; second, in semester-long academic courses in secondary schools in the United States, Canada, and several other countries; and third, in a semester-long course focused on political science and international negotiation for first-and second-year nonspecialist university students, including communication over the computer system with teams in Finland, Russia, Hungary, and Poland. The networking software in each setting is the same; the elements that structure the learning environment are parallel; the resulting interactions between students are also very similar. This chapter focuses on the Maryland Summer Center for International Studies, where the most intensive research has taken place.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".