Bibliographic record
Abstract
In this essay, inspired by the timeliness of our chronologically shared symbolic birth (the journal’s inaugural issue and my professional career as a faculty member) I briefly discuss and critically analyze the influence of community informatics upon my professional journey, in our mutual symbiotic growth, intersections and divergences. I also share my insights and reflections of the process that might influence readers’ own thinking about the past, present, and future of community informatics and its identity, intersections, growth, and more. This opportune moment allows me to draw upon intersections with my current work in social justice and inclusion advocacy and trace my privileged positionality to the “nurturing intellectual embrace” of community informatics that opened many proverbial doors and areas of impact over the years. In the process, I use evidence chronicled in my current curriculum vita that serves as a roadmap providing documentation “on the record” of milestones and activities referenced along the journey as I also develop ponderings of the past, current, and future emergence and growth of community informatics. In tracing key directions of growth through my own lens of experience and reality with community informatics shows how broader aspects in the larger society, academic world, and information field have strongly curtailed its emergence and adoption. Considering such factors directly might be helpful to shape its positive placement and positionality moving into the future.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.025 |
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".