Bibliographic record
Abstract
June 15, 2011 marked the date of the Vancouver riots that followed the Canucks loss of the Stanley Cup final. Social media as a form of communication between the public and police was a distinguishing feature during the 2011 riots, and is compared to the context of a similar Vancouver riot occurring in 1994. Through the review of literature on the criminal justice system, crowdsourcing, social media as a tool in policing, surveillance, language on Facebook and Facebook as a communication tool I explore the practice of communication as it unfolds on the Facebook group, “Vancouver Riot Pics: Post Your Photos” and examine the efficacy of this communication tool. The Facebook comments underneath the uploaded images are evaluated through a content analysis. Five Facebook images and there associated comment threads are collected in chronological order for the sample based on the outlined criteria of: 25-40 comments, a non-manipulated image, and being published in either the Globe and Mail or the National Post online news source. Erving Goffman’s theoretical orientation of frame analysis is applied to understanding the development of the Facebook comments; more specifically his concept of the social primary framework is directly related to the intended purpose outlined by the Facebook group. The purpose of “Vancouver Riot Pics: Post Your Photos” is to identify rioters through the public’s contribution of images and Facebook comments. Research findings suggest that the intended purpose of the Facebook group is achieved, as there is a significant emergence of the frames identification and crowdsourcing; therefore, Facebook is deemed a helpful tool in police investigation.
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 | 0.038 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".