The Two‐Culture Problem: Ecological Restoration and the Integration of Knowledge
Bibliographic record
Abstract
Abstract The terms “ecological restoration” and “restoration ecology” are frequently interchanged. Restoration ecology is the suite of scientific practices that constitute an emergent subdiscipline of ecology. Ecological restoration is the ensemble of practices that constitute the entire field of restoration, including restoration ecology as well as the participating human and natural sciences, politics, technologies, economic factors, and cultural dimensions. This paper is motivated by the concern that the broader practice of restoration may become narrowed over the next decade as a result of zealous attention to scientific and technological considerations, and that restoration ecology will trump ecological restoration. Scientific and technological acumen is necessary for successful restoration, but insufficient. Maintaining a broader approach to restoration requires respect for other kinds of knowledge than science, and especially the recognition of a moral center that is beyond the scope of science to address fully. An example of integrated restoration is presented: the ecological and cultural restoration of Discovery Island (near Victoria, British Columbia, Canada) by the Lekwungen people (Songhees First Nation).
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".