Uncovering Funding Inequities: Young Racialized Climate Justice Activists' Struggle within the Realm of Climate Philanthropy
Bibliographic record
Abstract
Global climate philanthropy remains chronically underfunded, with youth only receiving a mere 0.76% out of the global pool of climate funds. Racialized youth, including Black, Indigenous and People of Colour (BIPOC) receive even less than their white counterparts. This report delves into a year-long research project which explores how young racialized climate activists feel about this situation and what can be done about it. Through cellphilm making, that is, films made on cellphilms, 10 young racialized climate justice activists were given the opportunity to share their story, as well as their barriers and solutions on securing funding for their grassroots activism, projects and nonprofit organizations. These films were compiled together like a quilt, and the quilted cellphilm was later shared with 13 environmental funders and to 70 members in the Tkaronto community where there was feedback given and corresponding recommendations developed. The findings of the study highlighted the disconnect between funder requirements and the lived realities of young racialized climate justice activists who do not have the capacity for financial management and robust governance structures, among other requirements. The study further recognized the desire for funders to want to change their practices and allocate more funding in racialized communities. However, the bureaucratic resistance employees at these organisations might face whether needing to heed to their board, having the inability to participate in multi stakeholder events, and not having diverse staff hinder their progress to make systems change. Recommendations put forward for funders include funding more nonqualified donees, collecting demographic data on groups funded, creating more board positions for racialized youth, and spending down their endowments. The role of funder networks are essential for funders in moving the needle on these issues, so suggestions for these stakeholders include encouraging funders to support more nonqualified donees, training for funders so that they can change their practices and developing a conference fund for young racialized climate justice activists. This study shows promise in convincing environmental funders across what is currently Canada in making more accessible, inclusive and equitable decisions in the hope of a sustainable and just future.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".