Green Together: 2023 Progress Report on Implementation of the Transformative Climate Communities Program Grant
Bibliographic record
Abstract
(TCC) is an innovative investment in community-scale climate action, with potentially broad implications. Launched in 2017 by the California State Legislature, TCC funds the implementation of neighborhood-level transformative plans that include multiple coordinated projects to reduce greenhouse gas (GHG) emissions. The program is also designed to provide an array of local economic, environmental, and health benefits to disadvantaged communities, while minimizing the risk of displacement. TCC empowers the communities most impacted by pollution to choose their own goals, strategies, and projects to enact transformational change — all with data-driven milestones and measurable outcomes. The California Strategic Growth Council (SGC) serves as the lead administrator of TCC. At the time of this report, it had awarded 11 TCC implementation grants to 11 communities across the state (ranging from $9.1 million to $66.5 million per site). Additionally, SGC has awarded 25 TCC planning grants to communities that are in the early stages of forming a coalition to address local climate action goals (ranging from $94,000 to $300,000 per site). The state legislature has allocated funding to distribute two additional rounds of TCC grants.1 The UCLA Luskin Center for Innovation (LCI) serves as the lead evaluator in five communities that have received TCC implementation grants: all three Round 1 sites (Fresno, Ontario, and Watts), one Round 2 site (Northeast San Fernando Valley), and one Round 3 site (Stockton). LCI researchers are working with these communities to document their progress and evaluate the impacts of TCC investments. This progress report is the third in a series of five annual reports that will provide an overview of the funded projects, key accomplishments, and estimated benefits of TCC activities in the Northeast San Fernando Valley, collectively referred to as Green Together.2 This specific report documents progress through the end of FY 2021-2022, which overlaps with about 42 months of program implementation for leveraged projects (December 2018 to June 2022), almost 25 months of implementation for funded projects (May 2020 through June 2022), and three years of the COVID-19 pandemic. Leveraged projects were allowed to begin when the grant was awarded; funded projects could begin at the time of grant execution. Project partners’ responses to the pandemic are also highlighted throughout the report.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.041 |
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".