Knowledge mapping of protective clothing research—a bibliometric analysis based on visualization methodology
Bibliographic record
Abstract
In order to further understanding of the research status and fronts, a novel method was adopted in the textile and apparel field to perform knowledge mapping of protective clothing research over the last 20 years. The database of 1735 articles was built based on records retrieved from the Web of Science. Visualization software, CiteSpace, combined with Google Earth was applied to determine intellectual basis and research fronts for the protective clothing domain. Research area analysis indicated that the top ranked field was the “Materials Science” with a number of articles of 427. Publication distribution revealed that the Textile Research Journal was the most popular cited and citing journal of the protective clothing articles. The USA and China were the two primary countries contributing to the protective clothing research evidenced by the frequency, bursts and centrality. Donghua University, North Carolina State University and the University of Alberta, with a high publication frequency and centrality, were identified to be the main research drivers. The intensity of red nodes in the geographical visualization map proved the core status of Europe and America in the global cooperation network. According to the co-occurrence analysis, the three keywords of exposure, performance and heat stress were detected to be the most popular research topics over the last 20 years, corresponding to the study of exposure environment, performance evaluation and thermal physiology. The keywords in recent years suggested the research trend of enhancing the mechanism and fundamental investigation of the heat transfer process and fabrics.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.164 | 0.142 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".