Documenting and studying the use of assigned search tasks: RepAST
Bibliographic record
Abstract
ABSTRACT The Repository of Assigned Search Tasks (RepAST) is a searchable repository created through a systematic review of the interactive information retrieval (IIR) research literature. It currently contains bibliographic details for approximately 750 articles, including empirical studies that employ assigned search tasks and a smaller number of conceptual papers on task‐based searching. When available, the search task types, definitions and the task descriptions themselves are included. RepAST makes several contributions to the field. By bringing together examples of search task descriptions used in actual studies, RepAST provides a platform for studying practices within the research community and promoting greater conceptual clarity and consensus in the use of search tasks. To this end, the authors have published several studies based on analyses of the search tasks in the repository. In addition, researchers can use RepAST in a practical way, as a source of search task descriptions for reuse in new studies or in order to replicate prior research. In this interactive demo session, participants will have the opportunity to use the live RepAST system and provide feedback to the system designers.
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 | 0.073 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".