Justice Reframed? A Comparative Critical Discourse Analysis of Twitter Campaigns and Print Media Discourse on Two High-Profile Sexual Assault Verdicts in Ireland and Spain
Bibliographic record
Abstract
Analyses of media discourses on judicial verdicts in sexual violence cases offer critical insight into how this topic is mediated. This study explores post-verdict mainstream and social media reaction to two high-profile verdicts in sexual assault cases in Ireland and Spain: #IBelieveHer, launched in March 2018 following the acquittal of four men accused of rape in Belfast, and #YoTeCreo which coalesced online after five men were given a lesser sentence for sexual abuse in Pamplona in April 2018. This study first identifies the stance taken by mainstream media where verdicts were contrary to “popular” opinion. Secondly, it analyses dominant hashtags that emerged on Twitter following both verdicts. Finally, it traces similarities and differences in discourse patterns identified on mainstream and social media platforms across both countries. For analysis, we employed a Critical Discourse Analysis-based theoretical framework (e.g.,KhosraviNik 2017 KhosraviNik, M. 2017. “Social Media Critical Discourse Studies (SM-CDS).” In Handbook of Critical Discourse Analysis, edited by J. Flowerdew and J.E. Richardson, 583–596. London: Routledge.[Crossref] , [Google Scholar], “Social Media Critical Discourse Studies (SM-CDS).” In Handbook of Critical Discourse Analysis, 582–596) with resources from Framing Analysis (e.g.,Goffman 1974 Goffman, E. 1974. Frame Analysis: An Essay on the Organization of Experience. Vancouver: Harvard University Press. [Google Scholar], Frame Analysis: An Essay on the Organization of Experience. Vancouver: Harvard University Press) for methodological purposes. Findings suggest Spanish print media contained greater debate around legal understandings of sexual violence while the Spanish Twitter campaign was outward-oriented and explicitly feminist. #IBelieveHer displayed a narrower focus, with the “celebrity” dimension to this case contributing to a personalised, less nuanced, discourse on social and print media and more polarised discussion.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".