The Blameless and Blameworthy: Missing White and Indigenous Women and Girls' Social Construction on Winnipeg Police Service's Facebook Page
Bibliographic record
Abstract
Recently, police agencies have harnessed social media platforms, like Facebook, to communicate with the public regarding missing persons cases. I argue that the Winnipeg Police Service (WPS) is mostly absent from the social construction dynamics of missing women and girls. Instead, missing women and girls are socially constructed primarily through the comments and claims of Facebook users who draw on racialized stereotypes to imply these females’ responsibility and blame. Applying Valverde’s (2006) social semiotic template, I analyzed a purposive sample of 20 WPS Facebook posts about missing women and girls from 2019 to 2023 focusing on the selection of images, descriptive text, user comments and reactions. Results revealed that missing Indigenous women were constructed as most blameworthy for their disappearances, while missing Indigenous girls were constructed as less blameworthy, but not without some level of responsibility for their situation. In contrast, missing White women and girls were socially constructed as blameless ideal missing persons worthy of rescue. I conclude by reflecting on the theoretical and methodological implications of my study and offering directions for WPS social media policies to prevent the continued promotion of racial stereotyping and victim blaming.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".