Finding the Right Balance: A Reply to Jones’ Research on Building Communities of Practice Using a Wiki
Bibliographic record
Abstract
Are we expecting too much from a wiki? Reflecting on her lived experience of using a wiki in a graduate course, Jones shared a number of insights with regard to the capacity of using such technology in support of learning in a community of practice context. As she examined her experience and benefits, Jones identified tensions that impacted the social and communal aspects of what was shared in the wiki; she also explored the level of rigour that influenced the reliability of the content. Jones argued that wikis have potential in supporting the development of a community of practice only if specific social elements are addressed. As we reflect on our perceptions and expectations of developing and fostering learning through an online community approach, we need to carefully consider the balance between the affordance of the technology and the preparedness of students in terms of collaborative learning in community to foster knowledge building. “Use of the technology does not spontaneously cause communities to occur; communities of leaners must be planned” (Moller, 1993, p. 120).
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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 | 0.033 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.014 | 0.048 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.049 | 0.069 |
| Insufficient payload (model declined to judge) | 0.007 | 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".