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Record W2466930446 · doi:10.1177/1468017316656145

Methodological reflections on research with street youth

2016· article· en· W2466930446 on OpenAlexafffund
Jeff Karabanow, Sean A. Kidd, Tyler Frederick, Alan McLuckie, Jacqueline Quick

Bibliographic record

VenueJournal of Social Work · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of CalgaryUniversity of TorontoLakeridge HealthDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaNatureDalhousie University
KeywordsAcknowledgementFraming (construction)Reciprocity (cultural anthropology)SociologyQualitative researchResearch ethicsData collectionField (mathematics)Public relationsEngineering ethicsPolitical scienceSocial scienceComputer scienceGeography

Abstract

fetched live from OpenAlex

Summary This paper examines both the epistemological and practical limitations and challenges of data collection by reflecting on the experiences of a team of both junior and senior researchers engaged in such a longitudinal study. Findings This paper argues that longitudinal research with street youth challenges the boundaries and limits of the formal constructs of research and ethics that typically guide qualitative research by grappling with field issues such as navigating reciprocity, risk and authenticity within relationships with a vulnerable group. Application This paper calls for an explicit acknowledgement of the challenges researching populations such as street youth over time in our research ethics guidelines and encourages researchers to engage in dialogue leading to more reflective, transparent and accountable framing of how we collect data in the field with vulnerable youth populations.

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 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)0.027
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.960
GPT teacher head0.764
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes2
Has abstractyes

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