SIG/USE Research Symposium: Making Research Matter: Connecting Theory and Practice
Bibliographic record
Abstract
EDITOR'S SUMMARY Special Interest Group/Information Needs, Seeking and Use (SIG/USE) convened at the 2015 ASIS&T Annual Meeting to explore the links between theory and practice in information behavior. In his keynote address, Ross Todd urged the audience to go beyond models and aim for synthesis and meta‐analysis, focusing on the user. Lightning talks addressed a social cognitive theory analysis of a program for disadvantaged youth; adults with limited literacy and health information; mobile information workers; forming a community of practice; and information sharing practices among online communities. A key takeaway was that research should actively involve communities and their members rather than simply being about them. Safiya Noble's keynote highlighted hidden biases in automated search engine returns with encouragement to design algorithms enabling users to opt in or out of filtered returns. Attendees explored the topics raised further during a mixer chat and table talks. The symposium ended with presentations for the best paper, poster and research proposal and awards for student and international conference travel.
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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 | 0.092 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".