The Power of Domestic Violence and Abuse Counter Narratives: Telling Stories in Parliamentary Debates
Bibliographic record
Abstract
This paper sets out to interrogate the use of master and counter narratives in UK Parliamentary Select Committee debates surrounding the passage of the Domestic Abuse Bill (now Domestic Abuse Act 2021) in Parliament. These debates are a site that allow for the telling of counter narratives in order to challenge the narrative of the normative socio-legal position regarding domestic violence and abuse (DVA). With its roots in the patriarchy and stereotypical gender roles that foster violence and abuse, the issue with such a narrative is that it fails to recognise the complex, nuanced nature of domestic violence and abuse. As a result, it maintains the status quo and is disconnected from the realities of DVA. The work of this paper, then, is to consider the dialogue between the masterplot of DVA, the Domestic Abuse Bill, and the attempts of counter narratives to act as discursive resistance. It will consider what is the true power of these anti-hegemonic stories in exposing the problems with the master-narrative. Counter narratives submitted by organisations, activists, academics and survivors of DVA during the Committee Stage of the debates were not uniform, monolithic or pure, and were often plagued with inconsistencies and contradictions with one another. Characterised as the same but different, these counter narratives did not act in strict opposition to the hegemonic master narrative. As a result, this paper will draw on a case study which examines three different reports submitted during the Committee Stage of the Debates to consider the following questions: in what way can counter narratives act as discursive resistance in law reform efforts and why are some more successful than others in dismantling the master narrative through the mechanism of the law? Overall, the argument put forward is that it is the counter narratives with a greater illocutionary force and greater narratological power which can be a successful tool in law reform and effect a shift in the master narrative of domestic violence and abuse.
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 | 0.027 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".