Bibliographic record
Abstract
Conceptualized because I found a paucity of standardized requirements for fundraising positions, this exploratory/descriptive research study explored whether fundraising is an emerging profession in the context of Ontario’s English language Colleges of Applied Arts and Technology (CAAT). The study relied on both quantitative and qualitative data through a survey and interviews with five participant groups: college knowledge leaders, fundraisers and teachers in eight Ontario colleges, students enrolled in a full-time fundraising program, and donors who had contributed to an Ontario college within the previous five to 10 years. Focusing on fundraising post 1995, the study sought to explore the extent to which the five classical traits of professions identified in the literature, were evident in Ontario college fundraising activity, namely: autonomy, a formal course of study, a unique body of knowledge, professional associations with a formal code of ethics, and state regulations. The sample size was relatively small because of the focus of the research. Additionally, the findings of the study are not generalizable, as participants were not randomly selected from the population. However, as an exploratory/descriptive study and because of the paucity of scholarly data related to the specific research focus, the findings will add to an overall understanding of the topic. The literature review for the research revolved around the history of philanthropy in Canada as it relates to laws and taxes, origins of fundraising in Ontario colleges, and traits or characteristics that define a profession. The findings concluded that fundraising is not yet a profession, but it is in the process of evolving into one as some traits associated with a profession were visible in the data; other traits were at various stages of development. In fact, when fundraising in Ontario colleges was analyzed for the presence of trait criteria, I found that none of the requirements of the identified characteristics were fully met. The study concluded that there was room for more research in the area of fundraising as a profession in Ontario and there was a need for the development of Canada-specific fundraising policies as well as deeper inquiry into the professionalization of fundraisers.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".