Bibliographic record
Abstract
This chapter uses intersectionality theory and practice to analyze the gendered nature of armed conflict, particularly with respect to understanding the construction of conflict-related identities. It argues that gender, as an intersectional concept that encompasses masculinity, plays an important role in constructing an understanding of a conflict. The chapter begins with a basic overview of intersectionality as it applies to conflict settings. Looking at both the conflict in Northern Ireland and German reunification, it provides examples of how applied intersectionality can enhance understandings of gender, race, and class. The chapter discusses the importance of institutional responses to underlying structural inequalities that are often revealed by intersectional analysis. It closes with an examination of how intersectionality functions as a legal tool, examining a police strategy for the protection of Muslim women in Canada, as well as a study of conflict-related female fatalities in Northern Ireland.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".