Towards positive information science?
Bibliographic record
Abstract
Abstract This panel offers a refreshing counterpoint to the predominantly problem‐oriented perspective of theory and research in information science. Drawing inspiration from the fields of positive psychology and sociology, we explore the idea of a positive information science. This line of inquiry focuses on the positive qualities of information systems and the positive characteristics and habits of information users, as well as on the positive contexts of or factors in information phenomena. Insights into positive information phenomena provide a benchmark and target for improving information environments. The positive perspective also reflects a new generation of information‐users who harbor an upbeat sensibility concerning the tools and practices of the Information Age. The panel makes its case by offering an interdisciplinary comparison to positive social sciences, reporting results from two positively‐oriented investigations of information use in gourmet cooking and spirituality, and viewing the idea in the context of the Encyclopedia of Library and Information Science (Bates & Maack, forthcoming), an important benchmark and rubric of the field. To encourage a dynamic session, panelists and audience will see a list of positive features compiled and displayed in real time, serving as a basis for lively discussion.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.015 |
| 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; both teacher heads agree on what is shown here.
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".