MétaCan
Menu
Back to cohort
Record W4387338845 · doi:10.26522/ssj.v17i3.3433

“Our community needs to heal”: Using Photovoice to Explore Intergenerational Memories of Civil War with Young Central Americans in Toronto

2023· article· en· W4387338845 on OpenAlexaffvenueabout
Juan Carlos Jiménez, Morgan Poteet, Giovanni Carranza, Veronica Escobar Olivo

Bibliographic record

VenueStudies in Social Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsToronto Metropolitan UniversityMount Allison UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsPhotovoiceSociologyGender studiesImmigrationSpanish Civil WarCollective memoryMedia studiesPolitical scienceLawVisual arts

Abstract

fetched live from OpenAlex

In 2020, our research collective facilitated a photovoice project titled “Picturing Our Realities: Arts-based Reflections with Central American Youth in Canada,” which brought together young, second-generation, and one-and-a-half-generation (born in another country and moved at a young age) Central American identifying people in Toronto to talk about their experiences growing up as children of immigrants. This photovoice project reveals the ways the civil war and migration process is a haunting presence in the lives of second and 1.5 generation Central American Canadians as they grow up and carve their paths as adults. We can see how unresolved social conflict emerges and shapes family memory, sense of self, understandings of community, and means of engaging in community activism and community work. We argue that this act of remembering and paying homage to previous generations is a means of confronting and resisting past injustices and forming ways of healing from the afterlives of violence. This recognition of the afterlives of mass violence, and the calls of action that this recognition entails, may form a powerful catalyst for community organizing and creating community spaces to respond to historical hauntings and structural violence.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.775
GPT teacher head0.672
Teacher spread0.103 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueStudies in Social JusticeSame topicParticipatory Visual Research MethodsFrench-language works237,207