Bibliographic record
Abstract
Abstract At the end of a short journey, we can attest to the flourishing production of knowledge on gender and migration that has built up over the past 30 years in particular. Though we have on the whole referred to works in English, there is an extensive literature in other major languages, such as French, German, Italian and Spanish which have emerged from different social science traditions, in recognition of the significance of gendered migrations and feminist movements. English has come to dominate writing in this field (Kofman, 2020), ironically in large part through the European funding of comparative research as well as transatlantic exchanges (Levy et al., 2020). The past 20 years have been a rapid period of intellectual exchange in this field through networks and disciplinary associations, such as the International and European Sociological Associations or IMISCOE which supported a cluster on Gender, Generation and Age (2004–2009). The IMISCOE Migration Research Hub ( https://www.migrationresearch.com/ ) demonstrates the extensive production on gender issues and their connections with other theories and fields of migration. The economic and social transformations brought about by globalisation and transnationalism, and how its unequal outcomes and identities need to be understood through an intersectional lens (Amelina & Lutz, 2019), have heavily shaped studies of gender and migration (see Chap. 10.1007/978-3-030-91971-9_2 ). Indeed intersectionality has been suggested by some as the major contribution of contemporary feminism to the social sciences, and, has certainly been a theoretical insight that has travelled widely and rapidly from the Anglo world to Europe (Davis, 2020; Lutz, 2014) since it was defined by Kimberlé Crenshaw (1989). We should, however, also remember that it had antecedents in the writing of anti-racist feminists on racist ideology and sex by the French sociologist Claude Guillaumin (1995), on the trinity of gender, race and class in the UK (Anthias & Yuval-Davis, 1992; Parmar, 1982) and by scholars in Australia (Bottomley et al., 1991) and Canada (Stasiulis & Yuval-Davis, 1995).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.329 | 0.179 |
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".