MétaCan
Menu
Back to cohort
Record W7099787786

Prepared for: Millennium Ecosystem Assessment Bridging Scales and Epistemologies

2004· article· en· W7099787786 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsBridging (networking)Scale (ratio)SuiteCorporate governanceResource (disambiguation)Conceptual frameworkMultidisciplinary approach
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.264
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicInformation and Cyber SecurityFrench-language works237,207