Assessing the environmental practices and impacts of intentional communities: an ecological footprint comparison of an ecovillage and cohousing community in southwestern British Columbia
Bibliographic record
Abstract
The ecological footprint of the average Canadian is three times greater than the global per capita biocapacity of the planet. The purpose of this research is to gain insights from intentional communities on how to reduce household ecological footprints in Canada. Intentional community is an inclusive term for a variety of community types, including ecovillages and cohousing, where residents have come together to achieve a common purpose. Studies show that intentional communities have per capita ecological footprints that are less than those of conventional communities. I corroborate these findings through my own ecological footprint analyses of Quayside Village and OUR Ecovillage, in southwestern British Columbia. These communities have per capita ecological footprints that are smaller than some conventional averages. Overall, Quayside Village and OUR Ecovillage also have comparatively similar per capita ecological footprints, suggesting that residents of both urban and rural intentional communities may demonstrate similar environmental behaviours. Intentional community living is currently confined to small‐scale grassroots initiatives so even the aggregate environmental benefits are insignificant. Municipalities and land developers can help to advance the pro‐environmental practices of intentional communities by increasing incentives for this community model and adapting intentional community practices to a conventional context.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".