A toolkit for greater equity, diversity, and inclusion in early-career ecology funding
Bibliographic record
Abstract
Funding is critical in ecology and related fields, as it enables research and sustains livelihoods. However, early-career researchers (ECRs) from diverse backgrounds are disproportionately underrepresented as funding recipients. To help funding programs self-evaluate progress towards increasing equity, diversity, and inclusion (EDI) in their funding opportunities, we introduce the Stage-based Assessments of Grants for EDI (SAGE) Toolkit. Developed using existing literature, semi-structured interviews, and coauthors’ experiences, the toolkit considers how each funding stage (Advertisement, Application, Review, Awarding) interacts with applicants from racialized and other underrepresented backgrounds. The toolkit offers specific criteria and recommendations, with explanations and examples from funding agencies, to support applicants who have been historically marginalized in ecology and are often left out of equitable funding consideration. Changes in funding mechanisms alone will not reverse the marginalization of communities and peoples in the field of ecology, but advancing EDI must include action throughout the grant process. Efforts to increase EDI must be sustained, and the toolkit allows for additional considerations and evolving best practices. With the SAGE Toolkit, efforts to increase EDI can help to transition away from a transactional dynamic between funder and applicant to instead supportive community and collaboration. The SAGE Toolkit is available online at bit.ly/ediSAGEtoolkit.
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 | 0.065 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 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".