Bibliographic record
Abstract
Many case studies have examined Community Networks and we have at hand a good many rich and well grounded accounts of local experiences and outcomes as they have been observed in local circumstances. This sort of detailed, highly contextualized empirical work is essential to an understanding of contingent phenomena such as the performance of a Community Network. What we also need though, are theoretical approaches that are abstract enough to interpret the character and performance of differently situated Community Networks. The concept of community, the character of networks, and the implications of marrying the two, need to be teased out. To this end, I suggest that Community Networks be understood analytically as amodern hybrids that derive their ontological characteristics from a conflation of binaries. From this analytic perspective the Community Network is seen to be a sociotechnical assemblage that hybridizes the social and the technical, and not a set of technologies brought to bear on the social. The innovative feature of this particular form of sociotechnical assemblage, from an analytic point of view, is that it brings together community and network as both ontological concepts and as empirically observable phenomenon. The characterization of the assemblage as a community but also as a network is thus critiqued, and the differences between these two abstractions are explored, and it is further argued that the contrary ontology of the assemblage manifest structures that are at once heterarchic, and hierarchic.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".