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Record W2947367472 · doi:10.1080/17518253.2019.1609597

The Green Chemistry Initiative’s contributions to education at the University of Toronto and beyond

2019· article· en· W2947367472 on OpenAlexafffundabout
Alexander E. Waked, Karl Z. Demmans, Rachel F. Hems, Laura M. Reyes, Ian Mallov, Erika N. Daley, Laura B. Hoch, Melanie L. Mastronardi, Brian De La Franier, Nadine Borduas‐Dedekind, Andrew P. Dicks

Bibliographic record

VenueGreen Chemistry Letters and Reviews · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoMcGill University
KeywordsOutreachChemistryCurriculumRelevance (law)Chemistry educationLibrary scienceEngineering physicsMedical educationEngineering ethicsEngineeringSociologyPolitical sciencePedagogyPhysicsComputer scienceQuality (philosophy)Medicine

Abstract

fetched live from OpenAlex

The Green Chemistry Initiative (GCI) is a student-led group founded in 2012 with the primary mission of promoting green chemistry education at the University of Toronto. In order to achieve this, the GCI’s activities have included undergraduate curriculum development, arrangement of an external speaker seminar series, and organization of an annual three-day symposium along with biweekly trivia challenges. To broaden education beyond the Department of Chemistry, a successful YouTube video campaign articulating the Twelve Principles of Green Chemistry in an accessible manner has also been undertaken (acquiring over 40,000 views), in addition to monthly blog posts and conference/outreach presentations. Descriptions of these activities are discussed in this paper, along with the resulting impact they have had. Through such efforts, undergraduate and graduate students are showing a growing understanding of the relevance of green chemistry in today’s world, with the GCI serving as a platform for similar groups to build upon across Canada.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.943

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)0.0010.000

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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.

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

Citations15
Published2019
Admission routes3
Has abstractyes

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