Bibliographic record
Abstract
The 20th anniversary of The Journal of Community Informatics signifies a milestone in the evolution of community informatics (CI) as a field dedicated to empowering communities through the strategic use of information and communication technology (ICT). This article offers some personal reflections on the origins and evolution of CI, tracing its roots to seminal works by scholars such as Michael Gurstein. It also tells the story of how urban informatics was inspired by CI as a distinct field of scholarship to study the interplay between people, place, and technology in urban environments. Building on this foundation, the present challenges and opportunities facing CI are explored, including issues of digital inclusion, ethical implications of emerging technologies, and the transformative potential of ICTs for social change. Looking ahead, the article envisions desirable futures for CI grounded in a life-centred approach that acknowledges the interconnectedness of humans and non-humans within larger ecological systems. Embracing a more-than-human paradigm, CI is uniquely positioned to advocate for ecological justice, amplify the voices of marginalised human and non-human communities, and foster collaboration between humans and the environment to create and protect resilient and sustainable habitat for life on this planet. Through these efforts, CI can contribute to a more just, equitable, and sustainable future for all living beings, averting the planetary ecocide that threatens our shared existence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".