“She’s a Flagger, and I’m a Panner”: Exploring the Intricacies of Flagging, Panhandling, and Street Economies
Bibliographic record
Abstract
For survival, unhoused community members develop creative and alternative means for generating income, given most are excluded from the formal labor market. Of the various informal activities they engage in, few are more publicly visible than panhandling. Drawing upon 66 interviews with marginalized and street-involved persons in Winnipeg, Canada, we explore participants’ narratives and varied experiences with two distinct begging activities, “panning” and “flagging.” We unmask why participants chose specific activities and illuminate these activities’ structures, norms, and social dynamics. We show that while panhandling is a primarily solitary behavior, flagging is a highly organized and intricate type of informal labor characterized by social networks, cohesion, conflict and control over space. Accordingly, we discuss how social and environmental structures, norms, and dynamics can support and constrict marginalized people’s informal labor opportunities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".