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Record W4386836800 · doi:10.1177/237946152100700210

Institutional Policies for a Healthy Anthropocene Society

2021· article· en· W4386836800 on OpenAlexaff
Andrew J. Hoffman, P. Devereaux Jennings, Nicholas Poggioli

Bibliographic record

VenueBehavioral Science & Policy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnthropoceneEpoch (astronomy)Corporate governanceCapitalismEnvironmental ethicsParadigm shiftPolitical scienceSociologyBusinessEconomicsManagementPoliticsEpistemologyLawComputer science

Abstract

fetched live from OpenAlex

The Anthropocene epoch refers to the geological epoch, now underway, that is defined by monumental, human-caused geophysical changes in planetary ecosystems. Human society is also changing, marked by an equally profound shift in attitudes, beliefs, and practices. In this article, we apply research on social change in institutions—that is, in the enduring belief systems, ideas, and practices that guide organizations and society— to propose policies that could prepare Anthropocene society to change in ways that would ensure healthier ecosystems. These policies would alter the institutions driving corporate governance, patterns of consumption, the role of science in business and society, and the time horizons used by governments and organizations to plan, and they would help society adapt to unpredictable changes in the climate and in ecosystems. Ultimately, the policies would shift long-standing institutional structures, or logics, that support market capitalism and the belief in technology's ability to solve all problems to help create a more enlightened culture and more stable ecosystems on a rapidly changing planet.

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)0.016
metaresearch head score (Gemma)0.032
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 score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.011
Scholarly communication0.0100.007
Open science0.0010.006
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.668
GPT teacher head0.609
Teacher spread0.059 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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