Network Wisdom: The Role of Scaffolding in Expanding Communities of Practice and Technical Competencies in Community Networks
Bibliographic record
Abstract
‹ Volume 47 Issue 2, May 2022, pp. 271-291 › Articles Network Wisdom: The Role of Scaffolding in Expanding Communities of Practice and Technical Competencies in Community Networks Michael LithgowRelated informationAthabasca University Philip GarrisonRelated informationUniversity of Washington Esther Han Beol JangRelated informationUniversity of Washington Nicolas PacéRelated informationAlterMundi Michael Lithgow is Associate Professor at Athabasca University. Email: michael.lithgow@athabascau.ca. Philip Garrison is a PhD Candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Email: philipmg@cs.washington.edu. Esther Han Beol Jang is a PhD candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Email: infrared@cs.washington.edu. Nicolas Pacé is Community Networks Movement Builder at AlterMundi. Email: nicopace@altermundi.net Abstract Full Text References PDF EPUB Background: One of the key tensions to emerge from research on community owned and operated information and communications technology networks (“community networks”) is why some networks flourish while others fail. Analysis: These findings are based on interviews with 15 community network participants from four rural community networks in Córdoba, Argentina. Community network longevity is shaped by practices of scaffolding—knowledge sharing practices that expand what Étienne Wenger describes as “fields of negotiability” within communities of practice. Conclusion and implications: Network longevity was supported by scaffolding practices that decentralized technical capacities while encouraging deeper involvement among network participants. The network wisdom demonstrated in these cases appears to offer a promising strategy for community networks struggling to achieve longevity.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".