The Climate Literacy and Energy Awareness Network (CLEAN)
Bibliographic record
Abstract
It is important that we prepare tomorrow’s scientists, decision makers, and communities to address the societal and ecological impacts of a changing climate. In order to respond to, mitigate, and adapt to those changes, community members of all ages need accurate, up-to-date information, knowledge of the sciences, and analytical skills to make responsible decisions and long-term climate resiliency plans regarding these challenging topics. The Climate Literacy and Energy Awareness Network (CLEAN, http://cleanet.org) is 1) providing teaching resources for educators through the CLEAN Collection and pedagogical support for teaching climate and energy science and solutions; and 2) facilitating a professionally diverse community of climate and energy literacy community leaders, called the CLEAN Network, to share and leverage efforts to extend the reach and effectiveness of climate and energy education. This presentation will provide an overview of the CLEAN web portal and techniques we have used to reach educators. We will showcase the CLEAN Collection with its 750+ curated resources (curricula, activities, videos, visualizations, and demonstrations/experiments) that have been peer- and expert scientist-reviewed for scientific accuracy, pedagogical effectiveness, and technical quality. We will highlight the most recent updates: 1) we launched a new website design to increase accessibility, 2) we developed a collection for elementary-grade level resources and pedagogic support for primary teachers, 3) several efforts towards a more inclusive website (Spanish translations, culturally-responsive climate literacy principles, etc.), 4), teaching resources around the 4th National Climate Assessment, 5) a teacher ambassador program, 6) expanded partnership with NOAA’s Climate.gov portal and 7) virtual teaching resources. We will present findings usage data from our web analytics. Through analytics data, we will show lessons learned from CLEAN marketing efforts; insights which we anticipate can aid other climate and energy education programs in effectively increasing the visibility of their vital work.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".