Gender-based violence (GBV) against women with precarious legal status and their access to social protection in advanced welfare societies: an analytical contribution to reconstruct the research field and its institutional development
Bibliographic record
Abstract
Abstract The aim of this paper is to map the emergence and development of a research field around the topic of “gender-based violence (GBV) against women with precarious legal status and their access to social protection in advanced welfare societies”. We explore the academic knowledge production around this topic as a specific research field by using bibliometric data. We investigate the place occupied by scholars who publish in well-established journals, and their disciplines, in order to understand the relevance of different disciplines and groups of researchers in the knowledge production within the field. Our methodology includes analysis of co-authorship, cross-country collaboration, and co-citation. The search strategy is informed by discursive practices and knowledge production by influential international civil society actors (CSAs) involved in framing welfare responses to GBV against women with precarious legal status. Our results suggest that the knowledge produced in the field increased in terms of number of publications between 2010 and 2021, indicating a process of institutionalisation. Disciplines oriented towards certain groups of professionals such as clinical psychology, medicine, health, nursing, and social work, affiliated mainly to institutions in the US, Canada, and the EU, have a prominent role in knowledge production in this field. In our conclusions, we discuss the implications of these results in relation to gender studies and migration studies, along with some limitations of the use of bibliometrics software combined with an intersectionality approach.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.044 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".