Complexity Science and Global Change Workshop summary : Smithers, BC, Canada, February 13, 2009:
Bibliographic record
Abstract
"Complex systems science provides an inter-disciplinary framework for understanding and responding to global change phenomena. It seeks to understand the behaviour of whole systems and provides a common language and a suite of analytical tools that improve communication and integration across disciplines. Because it addresses whole-system behaviours that are beyond the scope of reductionist science, it can help to reconcile the culture clash between scientific and non-scientific approaches to understanding our world. In February 2009, the Bulkley Valley Centre for Natural Resources Research & Management (BV Research Centre) and the Natural Resources and Environmental Studies Institute (NRESI) of the University of Northern British Columbia co-hosted a half-day informal public workshop on Complexity Science and Global Change in Smithers, BC. The purpose of the workshop was to stimulate dialogue about complex systems science and how it can be applied to the challenges of maintaining sustainable ecosystems and communities in the face of global change. Presentations and lively discussion sessions focused on the relationships among complexity, diversity and resilience, genetic complexity in tree and salmon populations, restoring functional diversity in tropical forests, self-organisation in legal systems and managing natural resources under uncertainty. This document summarizes the presentations and discussions."
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.015 |
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".