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Record W4401333514 · doi:10.1080/01639625.2024.2385945

“She’s a Flagger, and I’m a Panner”: Exploring the Intricacies of Flagging, Panhandling, and Street Economies

2024· article· en· W4401333514 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDeviant Behavior · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsAthabasca UniversityUniversity of WinnipegUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFlaggingEconomicsEconomyAdvertisingPolitical scienceBusinessGeographyCartography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.387
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it