GÖZETİM ALANINDA İLETİŞİM ÜZERİNE YAPILAN ARAŞTIRMALARIN BİBLİYOMETRİK ANALİZİ: WEB OF SCİENCE (WOS)
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis in the field of surveillance and communication by analyzing 421 publications published between 2019 and 2024. The aim of the study is to determine the trends and orientations of the concept of surveillance in the scientific literature after Covid-19. To this end, the Web of Science (WoS) database was searched with the relevant keywords and 17,720 data were obtained. Among the data obtained, 421 articles published in English from the field of Communication were reached by applying the exclusion and inclusion criteria of the sample. The study was developed using Bibliometrix R-Tool and provided metadata from the WoS database. Publications, citations and information sources were analyzed, including top journals, top keywords, top cited and leading articles, top cited scholars, and top contributing institutions and countries. As a result, this study found that engineering, computer sciences and health are the most prominent fields related to surveillance, and that most articles are published in terms of publication type. In surveillance studies related to the field of communication, the words social media, privacy, Covid-19, China, algorithm and big data stand out; countries such as the USA, Australia, the UK, Canada and China are among the most productive countries; and with the increase in surveillance capacities, the protection of user privacy and security will become a critical issue.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".