Expanding biophilic design by virtual world-making: Indigenous-led perspectives and pro-environmental stewardship
Bibliographic record
Abstract
This paper acknowledges the critical need to prioritize pro-environmental stewardship, recognizing that the wellbeing of nature shares an intrinsic connection to our own. Working through an arts-based research (ABR) and Two-Eyed Seeing approach, we seek to explore more inclusive understandings of wellbeing that account for the rising implications of technology in human-nature relations, and expand the conceptual framework for biophilic design to encompass virtual world-making. Biophilic design has conventionally integrated natural elements and patterns into built environments, aiming to foster a sense of connection with nature and promote wellbeing. Here, we emphasize two Indigenous-led case studies in virtual world-making from Australia and Canada that demonstrate localized, experiential environments of nature, culture and storytelling: Bayi Gardiya (Singing Desert) (2019) by Christian Thompson, a Bidjara man of the Kunja Nation from central western Queensland, Australia; and Biskaabiiyaang, an Indigenous Metaverse project in development since 2021 under the leadership of Maya Chacaby, who is Anishinaabe, Beaver Clan from Kaministiquia (Thunder Bay, Ontario, Canada). We reflect on and discuss how these exemplars uniquely recognize virtual worlds as integrative with nature, culture, and storytelling. Ultimately, we highlight these projects for their design features that expand prospects for both biophilic design and pro-environmental stewardship.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".