Deconstructing Accounts of Intimate Partner Violence: Doing Interviews, Identities, and Neoliberalism
Bibliographic record
Abstract
This article engages in a reflexive, critical, analysis, re-examining data from an earlier project that used qualitative interviewing to investigate the experiences of women who came into contact with police because of situations of ‘verbal abuse’. In the present article, we use discursive psychology to explore how the women navigated narratives of abuse during the interviews; the ideological influences at play; and constructions of identity. During the interviews, the women worked to construct favourable social identities, by drawing on institutional discourse, direct, and indirect speech. Their narratives revealed the influence of neoliberal ideologies and practices on their understandings of intimate partner violence and their victim identities. While they were committed to a neoliberal worldview that emphasized individualism, they simultaneously recognized the need for police intervention. These contradictory ideological investments were reflected in the women’s fluctuating identity constructions as disempowered/empowered victim, and victim/perpetrator. We argue that such competing investments, for these women and the broader public, create ideological dilemmas which inhibit collective action towards social change.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.079 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".