MétaCan
Menu
Back to cohort
Record W3178751178 · doi:10.36367/ntqr.8.2021.547-553

Aprendiendo a Trabajar en Colaboración con Mujeres de Bajos Ingresos Económicos en la Ciudad de Kingston, Ontario, Canadá: Reflexiones sobre un Proyecto de Investigación Acción Participativa

2021· book-chapter· es· W3178751178 on OpenAlexaffabout
Pilar Camargo‐Plazas, Jennifer Waite, Martha M. Whitfield, Lenora Duhn

Bibliographic record

VenueNew Trends in Qualitative Research · 2021
Typebook-chapter
Languagees
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhotovoiceGeneral partnershipCitizen journalismContext (archaeology)SociologyHumanitiesPolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

This paper presents our experience following participatory methodologies to understand the experience of access to health and social services for women living on low-income in Kingston, Ontario, Canada.  Goals: The study aims are to describe and understand our experiences implementing a PAR study. Methods:  In this study, we use participatory (PAR), art-based research (photovoice), and interpretive (hermeneutic phenomenological) approaches. Results:  In this paper, we report our processes and experiences to date doing PAR with a not-for-profit organization in Kingston, Canada, both the challenges and the rewards, but most importantly the necessity of it for this context. This work has included the need to be adaptable and creative in light of COVID-19 restrictions, particularly while still striving to maintain study momentum. Conclusion: We have learned that a PAR study needs time, commitment and motivation. For meaningful, sustained change it must be enabled through partnership – one that honors and benefits those most in need.     

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)0.068
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0680.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0020.005
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0060.000

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 head0.633
GPT teacher head0.678
Teacher spread0.045 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueNew Trends in Qualitative ResearchSame topicParticipatory Visual Research MethodsFrench-language works237,207