ASIS&T annual meeting pre‐conference activities: SIG/USE research symposium context in information behavior research
Bibliographic record
Abstract
EDITOR'S SUMMARY ASIS&T's Special Interest Group/Information Needs, Seeking and Use (SIG/USE) met during the 2014 Annual Meeting for the group's 14th Annual Research Symposium, focusing on Context in Information Behavior Research. Keynote speaker J. David Johnson set the tone as he encouraged research into the context of information activities outside the usual settings and using varied theoretical perspectives and tools. Attendees also heard 14 lightning talks exploring conceptual and methodological issues. Speakers considered time and emotion as dominant contextual influences in information behavior, the role of information overload and ways diverse contexts affect seeking and providing information. Discussion of research methods encouraged a mixed‐method approach and analytic bracketing and illuminated how study participants create their own information context. During the world café session, tablemates discussed how information context is constructed and evolves, research gaps and available methodologies. The symposium ended with presentation of awards for outstanding research, best paper and poster and for travel to pursue studies. Gary Marchionini was recognized for his outstanding contributions to information behavior research throughout his career.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 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".