‘Man I’m all torn up inside’: Analyzing audience responses to <i>Making a Murderer</i>
Bibliographic record
Abstract
Despite the preoccupation with media depictions of crime and criminal justice, few studies have employed qualitative methodologies to investigate how audience members engage with, react to, and interpret media content. This analysis of Reddit forums dedicated to the 2015 Netflix documentary series Making a Murderer reveals the wide range of responses to the series and demonstrates the value of looking to user-generated content on social media platforms to understand how we think and feel about crime-related matters. I argue that Reddit users’ reactions to the series are consistent with existing research outlining the complexity of the public’s views regarding criminal justice policy and these responses are intelligible when we consider the broader socio-political context in which the series was released. Particularly important, in my view, are individuals’ affective responses – including empathy, confusion, heartbreak, anger, frustration, fear, and helplessness – and the ways these emotions are linked with beliefs about the efficacy of the criminal justice system and the purposes of punishment. Consequently, this article buttresses recent calls to consider the entire spectrum of human emotions and to investigate the ways these emotions are related to our multifaceted beliefs about crime and criminal justice.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".