A content and thematic analysis of Reddit discussions about When They See Us
Bibliographic record
Abstract
As the number of wrongful conviction media productions increases, an understanding of their impact on viewers is prudent. In this thesis, I investigated the effect of watching When They See Us ??? a dramatized miniseries depicting the wrongful conviction of five racialized teenagers in 1990 ??? on the nature and duration of Reddit users??? conversations about wrongful convictions. Qualitative and quantitative analyses were conducted on users??? posts. Posts about wrongful conviction were the third most frequently encountered (following posts reviewing the miniseries and mentioning other parties involved in the case) and thematically covered the issues of Risk Factors, Exoneration and Beyond, and the Innocence Movement. Additionally, wrongful conviction was discussed more during the release of When They See Us than before, suggesting that that these conversations emerged due to watching the miniseries, but were rarely posted six months afterwards. The findings are discussed in terms of slacktivism and implications for innocence advocates.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".