Technology Stewarding as a Medium to Develop and Sustain Niche Online Communities
Bibliographic record
Abstract
The goal of this chapter is to operationalize the theoretical argument about the importance of technology stewarding in the development of niche online communities. To bring about successful changes in a system, technology stewards propose technological solutions that will help to solve the problems of a community they know well. Assuming the role of a technology steward is the theme of a course in the M.A. in Educational Technology Program at Concordia University. Students enrolled in Social Computing and Computer Supported Collaborative Learning have twelve weeks to find a community that needs technological solutions and to propose such solutions to help them develop and to sustain their livelihood. The body of this chapter presents three vignettes, each consisting of a student project. Andre and Chantal’s vignettes both describe the creation of online learning communities with second language students, while Issa’s vignette describes the creation of an online support community of people suffering from the symptoms of a systemic illness. The three vignettes describe the approach in which each project was undertaken, the outcomes, and the lessons learned.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".