Crossing the divide: Putting information seeking research and theory into computer science practice to make information search systems and services more effective for the user
Bibliographic record
Abstract
Abstract With Carol Kuhlthau as moderator, we propose a panel of six information behavior researchers with diverse views on operationalizing findings and theoretical positions in information behavior/information seeking research for application in information system design and for re‐envisioning library and information services for technological information environments. Whereas computer‐science designed information systems and technological environments in libraries are designed for the user with an answer or at least the form of the answer firmly in mind, information seeking research is interested in the user with a complex information need who utilizes an information system or library service for knowledge construction and sense‐making. The dilemma is how to communicate information behavior/information seeking research and objectives to those who design the systems. The panelists propose different views on and solutions.
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 | 0.066 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".