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Record W172989284

Scale and Scope in Integrated Assessment: Lessons from Ten Years with Integrated Climate Assessment Model (ICAM)

2005· article· en· W172989284 on OpenAlexaff
Hadi Dowlatabadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScope (computer science)Scale (ratio)Identification (biology)Interface (matter)Field (mathematics)Natural (archaeology)Computer scienceData scienceEcologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Scale has traditionally been thought of in terms of the spatial extent and units of observation in a field. This is an excellent convention in the study of physical processes where scale also differentiates between the dominant forces at play. For example at the scale of planetary distances gravitation is the dominant force of interaction and the only thing that matters is mass, while at the atomic level electromagnetic forces dominate and charge of the bodies is critical. In this paper I would like to offer other criteria for scale selection in studies involving the interaction of social and natural systems. In this paper the focus is on integrated assessments where we hope to understand and capture the interaction between natural and social systems. By applying the same paradigm for scale identification as before, namely factors that dominate the dynamics and landscape of the system I would like to persuade the reader that we need to define two additional scales for integrated assessments: one to capture human cognitive processes and another to capture our social organization. The rationale for wanting to add these scales is simple. Awareness of the interface between nature and us is determined by our cognitive processes and technologies invented and employed to enhance these. Our ability to act on what we would like to do about the interface is shaped by the way our societies are organized and institutions invented and maintained to enhance them.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.309
Teacher spread0.284 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2005
Admission routes1
Has abstractyes

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