Bibliographic record
Abstract
The world of forest management is awash in buzzwords and acronyms—ecosystem-based management, adaptive management, Triad, emulation of natural disturbance (END), and latterly—resilience. Resilience is the concept du jour, and is increasingly employed as a catch-all term for a variety of management goals. There is peril in making excessive use of buzzwords as stand-ins for the complex goals that are the real target of forest management. In this paper, I explore the consequences of buzzword mutation, which leads to paradigm creep—the use of buzzwords far beyond their original sphere of application. Such inappropriate use threatens to dilute the meaning of the original metaphor and makes talking clearly about the legitimate targets of forest management more difficult. I use “sustainable development” as an exemplar of a buzzword that has mutated into uselessness. I then compare the descent of sustainable development with current trends in the use of “resilience”, and offer some guidelines for rescuing this term from a similar fate.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".