Identifying Information Retrieval Research Trends Using Author Co-citation Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p align="center"><strong>Abstract</strong> The aim of this study was mapping, visualizing and determining subject trends in the field of information retrieval using author co-citation network based on articles indexed in Scopus from 2005- 2018. This scientometric study was performed using co-citation analysis. Research population includes all articles indexed in Scopus in the field of information retrieval from 2005 to 2018. Therefore, 35018 papers were retrieved in this field. VOSviewer was used to analyze the author co-citation. The study indicated that a total of 604757 authors were co-cited, 212328 journals were cited. Also highly cited articles and sources were determined. Amongst countries, United States, China, United Kingdom, Germany and Canada ranked one to five, respectively. Computer science was a pioneer with regard to interdisciplinary area in IR. It is noteworthy that visualization of author co-citation in field of IR determined ten clusters, namely knowledge and information science, computer science, electronics, information retrieval, information seeking behavior, psychology, multimedia information retrieval, software engineering, ophthalmology and surgery.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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 it