GangstaLife: Fusing Urban Ethnography with Netnography in Gang Studies
Bibliographic record
Abstract
Abstract Recent research on street-involved populations has documented their online presence and has highlighted the effects of their online presentations on their lives in the real world. Given the increasing conflation between the online and offline world, contemporary urban ethnographers should pay increased attention to their participants’ online presence and interactions. However, methodological training of this sort is still in its infancy stages and has not yet evolved to guide the growing number of researchers undertaking this form of research. This article draws from our experiences using social media in our urban ethnographies with criminally involved groups, to examine the benefits, risks, and challenges of drawing on social media in urban ethnography. It is intended to serve as a foundational piece that will hopefully ignite scholarly dialogue, debate, and methodological training relating to deploying social media in urban—and specifically—gang ethnography.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".