تحلیل ساختار فکری مطالعات ربط در بازیابی اطلاعات در وبگاه علوم (Web of Science) طی سالهای 2009- 2018
Bibliographic record
Abstract
Aim:This research attempts to reveal the intellectual structure of “Relevance” articles in order to identify the top authors, countries and topical clusters, via co-word, co-author, and science visualization tools. Methodology:This is application research which is conducted by scientometric methodology. Population comprises 2530 records in the field of Relevance during 2009-2018, which had been retrieved from Web of Science. PreMap software is used for homogenization of words, and VOSviewer, and Bibexcel are used for science visualization. Finding:Findings indicate that the most articles were published in 2015 with 293 titles. The scientific map of co-authorship led to creation of 6 clusters. The top authors in scientific co-authorship were Huang, Jose, De rijke, Zuccon, Song, and Scholer. The top countries in scientific co-authorship were USA, China, England, Canada, India, and France. Use of co-word analysis led to the creation of 6 topical clusters in the field of Relevance, including among others: Information retrieval, Search engines, and Relevance feedback. Conclusion:The upward trend of scientific outputs in the field of Relevance during recent years indicates the increasing importance of this field in Library and Information Science. The results indicate that the researchers as well as studying the general and traditional principles of Relevance field have not been unaware of new developments in this field and studied both of them at the same time.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.020 |
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".