Human–Information Interaction—A Special Issue of the Journal of Informatics
Bibliographic record
Abstract
Every day, people from different professions and disciplines need to use information to make decisions, plan courses of action, discover patterns in big data, solve problems, analyze situations, make sense of phenomena, learn new concepts, make forecasts about future trends, and so on. People whose professions involve the frequent or continual performance of such activities include scientists, healthcare specialists, medical researchers, librarians, journalists, engineers, stock brokers, archeologists, educators, social scientists, and others—i.e., the so-called knowledge workers. As the amount and complexity of information is on the rise, it is becoming more important to understand how humans use and interact with information to support their everyday tasks and activities. [...]
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.019 |
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".