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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".