Delineation of Information Retrieval Research Area Using Input-Output Model
Bibliographic record
Abstract
Information Retrieval (IR) research has been one of the core research areas of Information Science (IS) and also a major source of interdisciplinary relations between IS and other disciplines, constituting a disjointed research area with fuzzy boundaries. The main purpose of this paper is to identify disciplines that contribute to IR research and to map the main features of the interdisciplinary structure of IR research area as a whole.La recherche dans le domaine du repérage d’information (RI) a toujours été un domaine-clé des sciences de l’information (SI) et également une source majeure de relations interdisciplinaires entre les SI et d’autres disciplines, constituant un domaine de recherche incohérent aux frontières floues. Le principal objectif de cette communication est d’identifier les disciplines qui contribuent à la recherche en RI et de mettre en correspondance les principales caractéristiques de la structure interdisciplinaire de la recherche en RI, de manière à constituer un tout.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".