Designs for Collective Cognitive Responsibility in Knowledge-Building Communities
Bibliographic record
Abstract
This article reports a design experiment conducted over three successive school years, with the teacher's goal of having his Grade 4 students assume increasing levels of collective responsibility for advancing their knowledge of optics. Classroom practices conducive to sustained knowledge building were co-constructed by the teacher and students, with Knowledge Forum software supporting the production and refinement of the community's knowledge. Social network analysis and qualitative analyses were used to assess online participatory patterns and knowledge advances, focusing on indicators of collective cognitive responsibility. Data indicate increasingly effective procedures, mirrored in students' knowledge advances, corresponding to the following organizations: (a) Year 1—fixed small-groups; (b) Year 2—interacting small-groups with substantial cross-group knowledge sharing; and (c) Year 3—opportunistic collaboration, with small teams forming and disbanding under the volition of community members, based on emergent goals. The third-year model maps most directly onto organic and distributed social structures in real-world knowledge-creating organizations and resulted in the highest level of collective cognitive responsibility, knowledge advancement, and dynamic diffusion of information. Pedagogical and technological innovations to enculturate youth into a knowledge-creating culture, with classroom practices to encourage distributed and opportunistic collaboration, are discussed.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".