Synthesis: building resilience and adaptive capacity in social–ecological systems
Bibliographic record
Abstract
Introduction A weekly magazine on business development issued an analysis of Madonna, the pop star, and raised the question ‘How come Madonna has been at the very top in pop music for more than 20 years, in a sector characterized by so much rapid change?’ A few decades ago, successful companies developed their brand around stability and security. To stay in business this is no longer sufficient, according to the magazine. You must add change, renewal, and variation as well. However, change, renewal, and variation by themselves will seldom lead to success and survival. To be effective, a context of experience, history, remembrance, and trust, to act within, is required. Changing, renewing, and diversifying within such a foundation of stability and maintaining high quality have been the recipe for success and survival of Madonna, and for rock stars such as Neil Young and U2. It requires an active adaptation to change, not only responding to change, but also creating and shaping it. In the same spirit, Sven-Göran Eriksson, coach of several soccer teams in Europe, claimed that it is the wrong strategy not to change a winning team. A winning team will always need a certain amount, but not too much, of renewal to be sustained as a winning team. Sustaining a winning team requires a context for renewal, or ‘framed creativity,’ borrowing from the language of the advertiser.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".