Mapping Intellectual Structure of Published Articles in Information Retrieval during 1983-2017.
Bibliographic record
Abstract
Abstract Today information and communication cause the daily growth of published information. Studying all scientific production content and structures for specialist in different fields and publications is impossible. This study aims to analyze the articles regarding information retrieval based on the concepts of co-occurrence network analysis and centrality indicators published in Clarivate Analytics Web of Science[1] during 1983-2017. This is a descriptive study, using Scientometric approach. Its statistical population contains all articles related to Information retrieval in Clarivate Analytics Web of Science during1983-2017. The scientific research on Information retrieval starts in 2002. Based on the scientific map of countries, America, England, Canada and Singapore have the most articles in information retrieval field. Iran and Brazil have also been active in research on this field from 2012. The top authors of Articles in IR field articles during 1989-2017 are: Spink, Boregman, Chowdhury and Meado. In the analysis of IR field articles based on co-word anlaysis, 8 subject cluster were observed. Among them related to internal and external factors in information retrieval.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.046 | 0.040 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".