Anatomy of Higher Education Fundraising in Canada
Bibliographic record
Abstract
Fundraising campaigns have made a significant difference in the communities they work within, for the causes and initiatives that matter to donors. Within Canada, we have one of the largest and most vibrant not-for-profit sectors, including charities supporting the arts, environmental protection, professional associations, health and education (Hall, et al., 2005). To support these campaigns, Canadians donated approximately $10.6 billion in 2020 to charitable organizations across the country, a number decreasing every year (Government of Canada, 2022). The potential then for a systematic approach in profiling is critical to success, allowing for a more targeted approach for increased fundraising success and measurement (Smith & Lipsky, 1993).\n\nWith over $22 billion dollars being donated online in 2010, an increase from around $7 billion dollars in 2006, online giving represents a significant portion of fundraising and continues to grow every year (Castillo, et al., 2014). Although there are large-scale philanthropic donations, there are many smaller donations that contribute to many organizations. Fundraising online creates a field where “equally important as the club of billionaires is to the future of philanthropy, so too are the contributions Americans of modest means channel through mass appeals that have so often worked in sync with large donations” (Zunz, 2012, p. 298-299). \n\nThe focus of this research is that identification, relationship and social capital influence supportive behaviours for any not-for-profit. Social media data was scrapped from Instagram and X accounts from a select group of Universities in Canada, and a data analysis was then applied used Python and VADER (Valence Aware Dictionary for sEntiment Reasoning) to understand sentiment, opinion and popularity of each accounts content. This work suggests that (1) marketing and communications practices are as important to not-for-profit organizations as they are for profit organizations, and this remains an area that it is a field of fundraising and communications practice that remains underserved, (2) that the factors that influence relationship in the alumni and student stakeholder groups are not utilized in communications strategy, specifically in social media groups and online communication, and (3) identify five potential strategies for communications success in fundraising and long-term post-secondary success.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".