Co-creating cultures of sustainability and co-imagining the teaching green building: the use of a participatory Photovoice process in a HPGB context
Bibliographic record
Abstract
Abstract Despite understanding the severity of the climate crisis, global action remains highly insufficient to address this challenge. Buildings are significant contributors to climate change due to their substantial global emissions, but can also contribute to urgent climate solutions. High-performance green buildings (HPGBs) can reduce operational building-level emissions dramatically, and potentially offer other benefits that support building users’ wellbeing and sustainable behaviours. HPGBs can provide useful environments to engage and influence culture and can act as publicly visible symbols of emergent local clean economies. However, a gap remains in knowing how best to support the emergence of citizen-led cultures of sustainability (COS) within green building spaces, an effort that could also help address the noted ‘performance gap’ of green buildings that has been linked to occupant behaviours. With the intention of investigating and supporting a growing citizen-led COS in a green building, this study applied an empowerment-based Photovoice method in the context of the HPGB evolv1, located in Waterloo, Ontario, Canada. Six building users (citizens) took and selected photographs, participated in facilitated group discussions and individual interviews, and contributed toward three public exhibitions based on research findings. Based on thematic analysis, findings suggest building citizens know what a COS means to them, and existing barriers and enablers within/around the evolv1 building toward achieving this. In addition, participants recognized the impact of specific green building features on their own personal sustainability-related values and practices, including the influence of sustainability symbolism within the building environment. Lastly, participants articulated specific recommendations for further promoting and growing a COS at evolv1. Significant themes identified are discussed in relation to and expansion of Cole (2014)’s Teaching Green Building (TGB) Model for Learning, providing preliminary insights into the degree to which evolv1 may or may not presently embody key aspects of a TGB. This study contributes to deepening understandings of how researchers and building citizens can support the emergence of COS within green buildings and related environments, with key takeaways that can be usefully applied to other settings, and theoretical and practical implications. Research findings encourage action toward supporting engaged, citizen-led COS, to advance more sustainable futures.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".