A Science Mapping Analysis of Sixty-Seven Years of Scientific Evolution about the Transgender Population
Bibliographic record
Abstract
Gender and identity issues permeate society as a whole. Therefore, the matters involving transgender individuals should be analised in order to understand the difficulties experienced by this population and the social practices implemented. In this sense, the objective of this study was to investigate the strategic themes and their evolution in relation to the theme. For this, a bibliometric performance and network analysis (BPNA) was carried out with the existing data in the Web of Science database between 1954 and march 2021. Twenty-three thousand and four hundred and seventy-one (23,471) articles were identified, which were included in the SciMAT software to perform a bibliometric analysis, resulting in the graph of the thematic evolution structure and the strategic diagram, in which 8 motor themes and a cross-cutting theme of great magnitude are highlighted, which are discussed in depth. The results show the relation between the transgender theme and gender, identity, sexual orientation, hormone therapy and gender-affirming surgery. It is concluded that, despite the large number of associated researches, some areas of study are still incipient, such as the inclusion of transgender people in the formal labor market and in the prison context, thus opening field for further studies.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.090 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".