Digital sociology and information science research
Bibliographic record
Abstract
ABSTRACT Digital sociology is a new subfield of sociology that has challenged the discipline to engage with digital methods, information practices, and questions of societal data use. Such topics have been central to information science research for decades; however, renewed attention being called to them by sociologists creates both opportunities and challenges for information researchers who incorporate sociological theory or methods into their work. This panel invites audience members to consider the emergence of digital sociology and to explore what it means for information science research. Panelists, drawing on a variety of disciplinary roots, will introduce and contextualize the idea of digital sociology, explore issues related to the existing and potential intersection between digital sociology and information science, and examine tensions and criticisms related to sociological digital information research. Following a brief introduction and presentations from four panelists who are themselves using or exploring digital sociology in information research, there will be an open question and answer session with the audience, followed by World Café style small group discussions designed for attendees to share perspectives, make connections with each other, and discuss methods for future research with a sociological lens.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".