Follow the money: Using an equity lens to create gift acceptance policies
Bibliographic record
Abstract
Follow the Money is a seminal study led by three graduate students who recently completed the Master of Philanthropy and Nonprofit Leadership (MPNL) at Carleton University, Ottawa, Canada. The study examines charitable gift acceptance policies and practises, with a focus on universities, and if and how existing policies account for the nuanced considerations related to: ethics; equity, diversity and inclusion (EDI); and reconciliation, decolonisation and Indigenisation when working with donors or potential donors. In completing this research, they identified and collated a number of practical considerations for fundraising and advancement professionals to consider when creating or revising their gift acceptance policies, including ideas around how to build fundraising cultures and practises that centre ethics, EDI, reconciliation, decolonisation and Indigenisation. This represents the top recommendations that are relevant for any charitable organisation to consider, whether in higher education or, otherwise, in developing or revising their gift acceptance policies and practices. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".