LEGAL LIABILITY IN FUNDRAISING: A NEW APPROACH IN RISK MANAGEMENT
Bibliographic record
Abstract
There are a number of practical steps that can be taken to avoid legal liability resulting from fundraising programs. This Charity Law Bulletin (“Bulletin”) discusses the need for charities to employ a proactive risk management approach and is directed to fundraisers and senior managers who either work for or on behalf of charities, as well as to lawyers who advise charities or who themselves serve on the boards of charitable organizations. For a more detailed discussion, you are directed to a paper by the author, entitled, “‘Looking a Gift Horse in the Mouth’: Avoiding Liability in Charitable Fundraising,” presented April 16, 2004, to the Canadian Association of Gift Planners, available at http://www.charitylaw.ca.
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 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.031 | 0.030 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".