Ensemble Research: A Means for Immigrant Children to Explore Peer Relationships Through Fotonovela
Bibliographic record
Abstract
This work began with a question about the challenges of nonverbal communication across cultures for both immigrant children in Canadian schools and a community of researchers. The question led to the gathering of an ensemble of researchers that included both adults and children. This article represents that collaborative group’s approach to a research innovation focusing on the fotonovela as both a research tool and a product of the research process. Antecedent narratives tell of the research team’s diverse skills, which became resources for the visual inquiry of immigrant children into their first Canadian school experiences. Combining digital-documentary, tableau, and digital-image manipulation, the children created, reflected on, and responded to fotonovelas about their peer relationships. Their stories combine elements of the personal with social symbolic representations that result in multiple layers of identification for the students and other readers of their research. This layered narrative is discussed as a unique result of combining digitized photographic processes with the fotonovela format. It also provides insights into how the fotonovela format can be used as a research tool.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".