MétaCan
Menu
Back to cohort
Record W3184459359 · doi:10.1017/s0376892921000254

Is the Anthropause a useful symbol and metaphor for raising environmental awareness and promoting reform?

2021· article· en· W3184459359 on OpenAlexaff
Nathan Young, Andrew N. Kadykalo, Christine Beaudoin, Diana Hackenburg, Steven J. Cooke

Bibliographic record

VenueEnvironmental Conservation · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsSymbol (formal)RecreationPoliticsMetaphorAction (physics)WildlifeEvent (particle physics)PandemicEnvironmental ethicsPolitical scienceCoronavirus disease 2019 (COVID-19)Public relationsSociologyEcologyLaw

Abstract

fetched live from OpenAlex

Summary Lockdowns associated with the COVID-19 pandemic temporarily restricted human activity and removed people from many places of work and recreation. The resulting ‘Anthropause’ generated much media and research interest and has become an important storyline in the public history of the pandemic. As an ecological event, the Anthropause is fleeting and unlikely to alter the long-term human impact on the planet. But the Anthropause is also a cultural symbol whose effects may be more enduring. Will the Anthropause inspire people and governments to mobilize for meaningful reform, or does it present a misleading and too-comforting portrayal of resilient nature and wildlife that could ultimately discourage action? While it is too early to gauge the impact of the Anthropause on human behaviour and politics, we use existing research on environmental symbols and metaphors to identify factors that may influence long-term behavioural and political responses to this globally significant period of time.

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)0.003
metaresearch head score (Gemma)0.005
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: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.026
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.305
Teacher spread0.256 · 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
GenreCommentary

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

Citations20
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental ConservationSame topicCOVID-19 impact on air qualityFrench-language works237,207