MétaCan
Menu
Back to cohort
Record W4220777606 · doi:10.1002/essoar.10510740.1

Geoscience Education Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

2022· preprint· en· W4220777606 on OpenAlexaff
Sarah K. Fortner, Cathryn A. Manduca, Hendratta Ali, Casey M. Saup, Samuel Cornelius Nyarko, Shannon M. Othus‐Gault, Viranga Perera, Vincent C. H. Tong, Anne Gold, Tanya Furman, Leilani Arthurs, Bridget K. Mulvey, Kristen St. John, Joel Singley, Elijah T. Johnson, M. R. Witter, R. L. Batchelor, Deron T. Carter, M. C. Damas, Lynsey E. LeMay, Karen M. Layou, Russanne Low, Huihui Wang, Kai Olson‐Sawyer, Amy Pallant, Katherine Ryker, Laura Lukes, Nicole LaDue, Katrien van der Hoeven Kraft

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWorld Wide WebElectronic mailIconComputer science

Abstract

fetched live from OpenAlex

Practitioners and researchers in geoscience education embrace ICON (Integrated, Coordinated, Open science, and Networked) principles and have a history of using them to create and share educational resources, to move forward collective priorities, and to learn from one another. Geoscience education brings substantial expertise in social science research and its application to building individual and collective capacity. This can be used to support ICON processes and improve the coproduction of knowledge between geoscientists and diverse communities. Geoscience is an important part of the knowledge needed to advance equity at local to global scales. The geoscience education community has expanded its own ICON capacity through access to and use of shared resources and research findings, enhancing data sharing and publication, and leadership development. We prioritize continued use of ICON principles to develop effective and inclusive communities that increase equity in geoscience education and beyond, that support leadership and full participation of systemically non-dominant groups, and that enable global discussions and collaborations.

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)0.027
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.008
Science and technology studies0.0020.001
Scholarly communication0.0120.001
Open science0.0210.031
Research integrity0.0000.001
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 head0.155
GPT teacher head0.452
Teacher spread0.297 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicScientific Computing and Data ManagementFrench-language works237,207