A Bibliometric Analysis of Deep Web Research during 1997 to 2019
Bibliographic record
Abstract
This study provides a bibliometric account of global deep web research published from 1997 to 2019. A total of 1995 records were imported from the Scopus database in a bibtex file. The bibliometrix package in RStudio was used for analyses. Publication Growth, Citations, Authorship, Country and Affiliations has been analysed. It was found that deep web research had a slow growth rate. In the last four years it has seen a recovery in the growth rate. Furthermore, this study shows the distribution of highly cited papers in the field over 23 years. It shows the country and institutional affiliation pattern of prolific authors. It also presents the most preferred sources, search terms and preferred medium of research communication. It is found that deep web research had a slow growth rate, but since 2016 it is picking up. China is the leading contributor of publications followed by the United States of America, Japan, and the United Kingdom. India is the fifth largest contributor. Contribution of citable publications has been led by Canada and USA with 81.9 per cent of efficiency followed by Australia (79.7 %), France (73.4 %) and Spain (73.1 %). It is also found that most of the prolific authors (by number of publications) do not appear in highly cited publications’ list. Deep web researchers mostly preferred using conference publications to communicate their findings. ‘Machine Learning’ and ‘cryptomarkets’ are two contemporarily popular terms being used by deep web researchers also, which indicates interest towards these topics.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.119 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".