Power, Communities, and Community Informatics: a meta-study
Bibliographic record
Abstract
In this paper we tackle the theme of power in the context of communities and Community Informatics through addressing five questions.What is power? What is empowerment?In what ways is it exercised?How does all of this pertain to communities?How does all of this pertain to Community Informatics?The first three questions are addressed through reference to well known propositions drawn from social theory, and having established this background, the last two questions are foregrounded through a content analysis meta-study of the abstracts of papers submitted to the 2009 Prato Community Informatics Conference. The overarching theme of this conference was power and empowerment in community informatics ruixkkcdc In this paper we tackle the theme of power in the context of communities and Community Informatics through addressing five questions. What is power?What is empowerment?In what ways is it exercised?How does all of this pertain to communities?How does all of this pertain to Community Informatics? The first three questions are addressed through reference to well known propositions drawn from social theory, and having established this background, the last two questions are foregrounded through a content analysis meta-study of the abstracts of papers submitted to the 2009 Prato Community Informatics Conference. The overarching theme of this conference was power and empowerment in community informatics.
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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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".