A Community Informatics Research Network Vision Statement
Bibliographic record
Abstract
Community Informatics (CI) became known in the late 1980s and early 1990s as a response to community needs. Within the field, community advocates and scholars utilize evolving technologies to foster community engagement and empowerment. Ideas for CI have been created, disseminated, and promoted in large part because of the Community Informatics Research Network (CIRN). This paper investigates the evolution of CI and the essential role played by CIRN in shaping the field’s trajectory. Collaborator interaction and complex socio-technical linkages are emphasized throughout the text, which highlights the worldwide, interdisciplinary nature of CI. Further, the paper investigates the accomplishments and shortcomings of the CIRN community and ultimately underscores the need for a comprehensive vision statement as we enter our next 20 years. The development process of this vision statement, along with CIRN values, mission, strategies, key literature, conference themes, and a CI declaration, are outlined. Through examining the historical roots of CI and CIRN, the paper provides insights into the ongoing dedication and development of global, inclusive, ethical, and culturally sensitive practices. The proposed vision and mission reflect the aspirations of the CI community, providing a roadmap for navigating the complexities of society, technology, and global collaboration. The strategic efforts are intended to improve research, practice, teaching, accessibility, and inclusion in the field of CI. This comprehensive vision aims to empower communities globally via the wise use of information and communications technology.
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 | 0.114 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.031 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.025 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".