Tracking the Field, Volume 3: Exploring Environmental Grantmaking
Bibliographic record
Abstract
The third volume of Tracking the Field continues the advancement of data collected, analyzed, and presented to build a better understanding of the environmental philanthropic field. This data is fundamental to understanding environmental philanthropy overall. This volume captures U.S. foundations' initial response to the current economic crisis that began in 2008, and includes new innovations and a more comprehensive analysis of issues, strategies and global grantmaking by Environmental Grantmakers Association (EGA) members. Tracking the Field is part of a growing body of research of environmental funding trends across the globe, which includes Europe, Australia, and Canada. This report focuses both on overall U.S. environmental funding, as well as a deeper exploration of the EGA membership's grantmaking. Together with the interactive database available to members through EGA's website, Tracking the Field represents an evolving and innovative tool to enhance EGA members' ability to increase knowledge, coordination, and collaboration for enhancing the field.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".