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Record W3172670012

Changing Climate Change: Examining efficacy of community based initiatives and micro-scale climate action

2021· article· en· W3172670012 on OpenAlexaff
Maya Willard-Stepan, Allie Fong, Yehia Sabaa

Bibliographic record

VenueUndergraduate Research Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsGreenhouse gasClimate changeGlobal warmingNatural resource economicsEnvironmental scienceAgricultureReforestationEcological forecastingScale (ratio)Environmental resource managementEnvironmental planningEnvironmental protectionBusinessEconomicsGeographyAgroforestryEcology
DOInot available

Abstract

fetched live from OpenAlex

It is well established that global warming surpassing 1.5-2°C above pre-industrial levels will cause irreversible damage to our world. The adverse rise in global temperatures is accelerated by anthropogenic activity such as greenhouse gas emissions and environmental degradation. While certain scenarios have been projected to significantly lower global warming rates, most of these developments will require immediate global top-down policy shifts. Several international treaties and agreements have been created to combat climate change. Nonetheless, these remain ineffective at creating meaningful progress and cast doubt on how realizable a positive climate scenario is. In this review, we analyze how regional policies and actions combat the climate crisis by examining how specific community initiatives impact climate indicators such as reforestation, greenhouse gas emissions reduction, and sustainable agriculture. Our findings conclude that local initiatives have shown more immediate success compared to their global counterparts. Thus, additional locally led climate initiatives is warranted.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.174
GPT teacher head0.389
Teacher spread0.215 · 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.

Study designObservational
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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