Bibliographic record
Abstract
Social informatics researchers use a variety of techniques to explore the intersections between technology and society. Current interest has turned to making more explicit our commonly tacit knowledge processes that involve people and technology. Knowledge creation, sharing, and management processes are commonly hidden, and this is even more the case regarding ignorance processes such as the denial and obfuscation of knowledge. Understanding the construction, generation, and perpetuation of ignorance can: (i) provide insights into social phenomena that might otherwise seem inexplicable (for instance, persistence of “urban myths”), and (ii) enable development of interventions to either facilitate (as with privacy‐sensitive material) or combat (as with malicious disinformation) ignorance. Although several pressing information issues relate to ignorance, agnotology (the study of ignorance) has only recently entered into the information science literature. An agnotologic approach expands the repertoire of methods and approaches in social informatics, better enabling the field to grapple with pressing contemporary issues of mis/dis/lack of information. Using Robert Proctor's typology of constructions of ignorance, this article describes ways that each type may be germane to and within social informatics, highlighting social informatics topics that would benefit from agnotologic exploration, and suggesting theoretical and methodological approaches useful to a social informatics of ignorance.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".