Selected ASIS&T board members discuss research trends in information science: A summary
Bibliographic record
Abstract
Abstract Editor's Summary A symposium organized by McGill University's iSchool students and director brought together past, current and future ASIS&T presidents and board members to discuss trends in information science research. The discussion revealed diverse opinions on the definition of information science, concerns about research practices and expected directions for future research. Definitions of the field focused on social questions in an information society, the intersection of information and technology and strategies to better connect information with users. Panelists exhorted attendees, many being students, to tackle big questions, consider the applications of their research and collaborate with other disciplines. The critical role and strength of information science should drive robust and compelling research, addressing areas of need from the personal to the national level. Specific topics needing investigation include big data, information security, information as a stimulus for creativity, personal information management and better integration with technology.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".