You’ve got mail: an analysis of direct mail and direct email fundraising letters
Bibliographic record
Abstract
In recent years, online fundraising (e-philanthropy) has grown to become a critical component of charitable fundraising in the Western world. The rapid development and proliferation of e-philanthropy means the need to critically investigate digital spaces as distinct communicative entities has now become necessary. Research in the field has revealed that digital documents differ from their hardcopy equivalents in terms of how they are consumed, since readers’ expectations vary when reading online vs. offline documents. The following research paper explores the direct mail fundraising letters and email appeal campaigns of three non-profit organizations operating in Toronto, Canada. Using Karen A. Schriver’s model for Document Design and Vijay Bhatia and Thomas A. Upton’s seven-move discourse structure for the direct mail letter genre, the goal of this MRP is to compare and contrast the traditional direct mail letter to its digital counterpart in order to identify the differences between the written rhetorical and visual document design strategy applied to each medium. A comparison between the printed and digital formats of the direct mail letters will hopefully provide a better understanding of how the traditional direct mail fundraising letter should be tailored for successful online consumption.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".