Prepared for: Millennium Ecosystem Assessment Bridging Scales and Epistemologies
Bibliographic record
Abstract
This paper represents the efforts of a group of scholars from multiple disciplines to synthesize current theoretical and empirical efforts to understand scale and cross-scale dynamics in solving environmental and resource management problems. Over the last year+, W. Neil Adger, Fikret Berkes, David Cash, Po Garden, Louis Lebel, Per Olsson, Lowell Pritchard, and Oran Young, have collaborated to produce a suite of papers on scale and cross-scale dynamics (see titles of these papers below). We met in Montreal in October 2003 to discuss our papers and collectively outline this overview paper. As such, this paper is designed to synthesize the work represented in the papers listed below. Given the timing of when first drafts of the papers were completed, this current synthesis draft does NOT adequately provide this synthesis. It highlights the major themes that run through the papers but does not yet effectively integrate the findings and conceptual development that is contained in the papers. We hope that feedback from the Alexandria meeting (where a number of these papers will be presented) will help us truly synthesize existing understanding for the next draft of this synthesis paper. We look forward to comments!-David Cash Papers to be submitted in a scale and cross-scale dynamics special issue of Ecology and Society: Scale and cross-scale dynamics: governance and information in a multi-level world (David W. Cash, W.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".