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Record W2031827346 · doi:10.1177/0270467609342703

Scale, Composition, and Technology

2009· article· en· W2031827346 on OpenAlexaffabout
Peter A. Victor

Bibliographic record

VenueBulletin of Science Technology & Society · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsYork University
Fundersnot available
KeywordsScale (ratio)Composition (language)Goods and servicesUnit (ring theory)Gross domestic productProduct (mathematics)EconomicsEnvironmental scienceEnvironmental economicsEconomyGeographyEconomic growthMathematics

Abstract

fetched live from OpenAlex

Scale (gross domestic product), composition (goods and services), and technology (impacts per unit of goods and services) in combination are the proximate determinants in an economy of the resources used, wastes generated, and land transformed. In this article, we examine relationships among these determinants to understand better the contribution that changes in each have made in the past, and might in the future, to reduce the burden placed on the environment by the economy. Using the IPAT equation we assess quantitatively historical changes in scale, composition, and technology in Canada and their respective contributions to changes in environmental impact. The Kaya equation provides a similar framework for a more in-depth consideration of the changes in scale, composition, and technology required to achieve a substantial reduction in global emissions of carbon dioxide. We conclude that it is essential to address scale as part of the solution.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.005
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.003
GPT teacher head0.216
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations5
Published2009
Admission routes2
Has abstractyes

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