MétaCan
Menu
Back to cohort
Record W3037518329 · doi:10.1109/mts.2020.2991495

Leveraging Digital Disruptions for a Climate-Safe and Equitable World: The Dˆ2S Agenda: [Commentary]

2020· article· en· W3037518329 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Technology and Society Magazine · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsMila - Quebec Artificial Intelligence InstituteImpact
FundersClimateWorks FoundationCanadian Institute for Advanced Research
KeywordsSustainabilityTransformative learningPolitical scienceProcess (computing)Engineering ethicsClimate changePublic relationsBusinessEngineeringComputer scienceSociology

Abstract

fetched live from OpenAlex

A new report, Digital Disruptions for Sustainability Agenda (the DA2S Agenda), developed by Future Earth's Sustainability in the Digital Age initiative is discussed in this paper. The DA2S Agenda was developed over the course of a year, engaging over 250 experts from around the world through workshops, online consultations, and desktop research. This article provides an overview of the analysis and findings outlined in the DA2S Agenda. We begin with an overview of the research on how to change systems and drive societal transformations. We then describe the process used to develop the DA2S Agenda and provide a summary of the research and innovations outlined in it. The final section outlines near-term actions needed to establish the enabling conditions to drive the transformative systems changes needed for a climate-safe and equitable world.

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.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.590

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 communication0.0000.000
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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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