SUCCEED: Summer Center for Climate, Energy, and Environmental Decision Making
Bibliographic record
Abstract
ABTRACT - Preparing a literate public to critically evaluate issues related to climate change, energy and the environment is an important pillar towards more sustainable societies. The Summer Center for Climate, Energy, and Environmental Decision-Making (SUCCEED) is a K-12 Outreach program created by - University’s Department of Engineering and Public Policy (EPP). The program was originally proposed and created under the auspices of the Climate and Energy Decision Making Center (CEDM), a multi-institution collaborative agreement anchored at- University, and supported by the U.S. National Science Foundation. The program objectives are a) to improve scientific literacy by providing a free summer program focusing on climate, energy, and environmental decision-making for both students entering tenth grade, and K-12 teachers, b) encourage pursuit of STEM-related careers, and c) to help teachers prepare curriculum in this area to be used in class. SUCCEED consists of two programs: a five- day workshop with approximately twenty students entering 10th grade, and a two-day workshop with approximately ten math and science educators to improve teacher curriculum. SUCCEED has been held every summer from 2011 to 2016, and is planned to be held again in 2017. Through this submission, we plan to describe the general characteristics of SUCCEED, discuss program outcomes, and explore lessons learned.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.037 |
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".