Does scale matter: using different lenses to understand collaborative knowledge building
Bibliographic record
Abstract
Web-based environments for communicating, networking and sharing information, often referred to collectively as Web 2.0, have become ubiquitous - e.g., Wikipedia, Facebook, Flickr, or YouTube. Understanding how such technologies can promote participation, collaboration and co-construction of knowledge, and how such affordances could be used for educational purposes has become a focus of research in the Learning Science and CSCL communities (e.g., Dohn, 2009; Greenhow et al., 2009). One important mechanism is self-organization, which includes the regulation of feedback loops and the flows of information and resources within an activity system (Holland, 1996). But the study of such mechanisms calls for new ways of thinking about the unit of analysis, and the development of analytic tools that allow us to move back and forth through levels of activity systems that are designed to promote learning. Here, we propose that content analysis can focus on the flows of resources (i.e., content knowledge, scientific artifacts, epistemic beliefs) in terms of how they are established and the factors affecting whether they are taken up by members of the community.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".