Social Informatics Perspectives on Emerging Technologies: The Way Forward
Bibliographic record
Abstract
ABSTRACT Early social informatics research focused primarily on ethnographic, site‐specific observations within organizations and was based on smaller case studies. The rising of social media and big data availability have made large‐scale data analysis accessible and easier. This has informed social informatics perspectives by examining the roles and impacts of social media in our work and social lives. The panel aims to utilize principles of social informatics approach to understand emerging issues related to social media, which are pervasive in almost every aspect of our daily lives, and to Information and Communication Technologies (ICTs) more broadly. To push social informatics research forward, the panelists will address the questions regarding the future of social 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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.028 | 0.057 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".