Bibliographic record
Abstract
Climate change has become a mainstream concern, with proposed solutions focusing on mitigating the impact of environmental degradation on human lives. I use Toronto’s High Park as a case study to explore why an essential aspect of achieving profound and enduring environmental restoration involves recognizing the deep interconnectedness between human beings and what we commonly refer to as “nature”. High Park’s black oak savannah was managed for thousands of years by Indigenous peoples, who used fire to maintain the savannah’s open canopy and activate seeds. European colonization halted these burns, leading to most of the savannah being lost to closed-canopy forests and invasive plants; globally, less than one percent of oak savannah ecosystems remain. Amid despair, we rekindle our hope through prescribed burns, stewardship programs, and the planting of grasses and wildflowers with long-stand relationships with the land; we know that hope is a discipline where collective commitment and action are essential. Indigenous knowledge and storytelling remind us of our responsibility of reciprocity to the land and all our relations. We sow these seeds of hope and trust they will flourish and generate new life, much like ancestral seeds activated by fire.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".