Transform Fresno: 2024 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 ad- administrator of TCC. At the time of this report, SGC has awarded 15 TCC Implementation Grants across five rounds of funding to 15 communities throughout the state (ranging from $9.1 million to $66.5 million per site). The state legislature has allocated funding to distribute one additional round of TCC grants.1 The UCLA Luskin Center for Innovation (LCI) serves as the lead evaluator for six communities that have received TCC Implementation Grants across the following funding rounds: all three Round 1 sites (Fresno, Ontario, and Watts), one Round 2 site (Northeast San Fernando Valley), one Round 3 site (Stockton), and two Round 4 sites (South Los Angeles and Stockton). LCI researchers are working with these communities to document their progress and evaluate the impacts of TCC investments. This progress report is the final in a series of five that will provide an overview of the key accomplishments and estimated benefits of TCC-funded activities in the City of Fresno, collectively referred to as Transform Fresno.2 This report documents progress through the end of fiscal year (FY) 2022-2023, which overlaps with about 15 months of post-award planning (January 2018 to April 2019), and 51 months of grant implementation (April 2019 through June 2023). Even though this report is the final progress report authored by LCI, Transform Fresno carries on, with implementation milestones that are expected to continue through October 2025.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.020 |
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".