Representations of Rape: Transcending Methodological Divides
Bibliographic record
Abstract
This article presents a mixed methods research design that embeds social network analysis, a quantitative method, into the qualitative approach, institutional ethnography, using an illustrative example of rape reporting. There are two practical outcomes of the embedded mixed methods research design, underwritten by the pragmatist paradigm: it offers a visual depiction of the ‘line of fault’ between the lived experience and its institutional representation, and it facilitates an analysis of the internal structure of the associated texts. Through its unusual blend of two methods, commonly considered to be at opposing ends of the methodological spectrum, the article advances the agenda of mixed methods and contributes to the recent debates about the nature and benefits of mixed methods research.
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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 | 0.074 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".