“The Heartbeat of Hamilton”
Bibliographic record
Abstract
Traditionally, children’s “voices” have been underrepresented in the field of cultural geography. Rather, “adultist views” dominate. In this article, we describe the methodological process of undertaking a comprehensive, participatory action visual methodologies project known as the Hamilton Photovoice Project (HPP) with children from low socioeconomic status neighborhoods in Hamilton, Ontario, Canada. We also discuss the lessons that we have learned along the way. The purpose of the HPP was to investigate how children in downtown Hamilton experience their metropolitan landscape. Specifically, we examined walking routes for the purposes of identifying desired environmental changes that may increase the use and enjoyment of community walking routes and spaces along routes. In doing so, we discuss what was learned from the methodological process of collecting and working with children’s visual productions, including how children appear to use visual methods. Although children’s visual productions appear to convey complex emotional, social, and political sentiments about their spatial experiences and desired environmental changes, the methodological process is invariably constrained by the institutions that govern and police children today during the research process. Thus, this study contributes toward the ongoing dialogue about the merits and tensions inherent to using children’s visual productions as a way to capture perceptions toward place.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".