Adapting CBPP Platforms for Instructional Use
Bibliographic record
Abstract
Commons based peer-production (CBPP) is the de-centralized, net-based approach to the creation and dissemination of information resources. Underlying every CBPP system is a virtual community brought together by an internet tool (such as a web site) and structured by a specific collaboration protocol. In this talk we will argue that the value of such platforms can be leveraged by adapting them for pedagogical purposes. We report on one such recent adaptation. The Noösphere system is a web-based collaboration environment that underlies the popular Planetmath website, a collaboratively written encyclopedia of mathematics licensed under the GNU Free Documentation License (FDL). Recently, the system was used to host a graduate-level mathematics course at Dalhousie University, in Halifax, Canada. The course consisted of regular lectures and assignment problems. The students in the course collaborated on a set of course notes, encapsulating the lecture content and giving solutions of assigned problems. The successful outcome of this experiment demonstrated that a dedicated Noösphere system is well suited for classroom applications. We argue that this “proof of concept ” experience also strongly suggests that every successful CBPP platform possesses latent pedagogical value. ∗Supported by Dalhousie’s Centre for Learning and Teaching, and N.S.E.R.C., Canada.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".