Bibliographic record
Abstract
As a prelude to my description of what stewardship means, there are three characteristics of the notion of “stewardship” that I attempt to maintain and view as essential elements of successful land stewardship (as opposed to preservation and protection). Stewardship implies privilege bounded by responsibility. Stewardship, historically, is an act of managing possessions or property for someone other than oneself. Hence it is, at the core, an act of altruism. So, for SVF, stewardship is about taking care of lands for future generations. Stewardship is about a deeply held inner conviction that motivates landowners and land managers to take good care of the land, not merely for personal gain, but for future generations and for the benefit of society. We must explicitly acknowledge what successful land stewardship is not—benign neglect as a strategy for maintaining or enhancing ecosystem health and native biodiversity is no longer a viable strategy for successful stewardship of biological resources. Therefore, land stewardship implies actions designed to sustain or accelerate the recovery of degraded ecosystems by complementing or reinforcing natural processes. With that in mind, here is my first stab at it: 1) Actions designed to emulate natural ecosystem structure and function; 2) A set of actions designed to promote beneficial human/wildland interactions compatible with the range of natural variability of native ecological systems. In the case of SVF, that refers primarily to redwood forest ecosystems; 3) To actively engage in the prevention of habitat loss by facilitating recovery in the interest of long-term sustainability (adapted from Fisheries and Oceans Canada, “Stewardship in Action” program). 4) Stewardship is land management that provides public benefit while envisaging other species as a form of “stakeholder” in management decisions. In some respects, the idea of “Planetary Stewardship” is a bit of a misnomer. Stewardship, it seems to me, is deeply rooted in the particulars of a place. To be a good steward, one must have a very clear and specific place or set of things to steward. Now of course, this begs the question, at what scale does one lose the particulars, and I have no ready answer. However, the examples of success and challenges are all rooted in particular places and relationships. I can image talking about a sustainable or healthy planet, but stewarding a planet is not, it seems to me, the sort of thing that is accessible through the act of stewardship. However, it is likely achievable through the compounding benefits of cumulatively successful stewardship efforts at the local level.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".