Climate Grief: Relearning the Future
Bibliographic record
Abstract
Prince Edward Island (PEI), Canada, is eroding at an average rate of about one foot per year. People are grieving the losses, both past and future, of meaningful places embedded with memory. While the field of landscape has often separated climate change adaptation projects from work focused on human healing, this thesis brings the two sides together in a community-centered landscape regeneration project on PEI. The meaning of loss is different for every individual on the island, whether human or non-human. At the root of environmental degradation is the property line, a legacy of ongoing colonial practices that continue to facilitate deforestation at the edge and exacerbate land loss. These imagined lines motivate landowners to try and stop erosion, and limit many other peoples’ access to the shoreline. However, steady levels of erosion are also part of a broader ecology of disturbance that supports biodiverse habitat. This project imagines how environmental strategies can be integrated across property lines to reweave the ecological gradient from the inland forest to the intertidal zone, creating new relationships with healthy erosion. As the fabric of the island is rewoven, human and ecological healing become intertwined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".