Online social network fundraising: Threats and potentialities
Bibliographic record
Abstract
Abstract There has been a growth in online fundraising from crowdfunding apps, like GoFundMe, that propagate fundraising appeals on social networking sites. In the online space, these crowdfunding apps pose a potential threat to the traditional intermediation role of charities. The disintermediation threat is that donors choose crowdfunding intermediaries instead of charities to channel their giving. In this article, we discuss what makes crowdsourced fundraising effective and how charities can adapt to this new dynamic for more effective online fundraising emphasizing two key success factors: brand strength/reputation and managing the donor experience. In addition, we explain the advantages and disadvantages of social media fundraising and giving and propose ways charities can leverage their good reputations and public trust to stimulate reintermediation. Finally, we propose a landscape for future research based on model that emphases the fundraising campaign's ability to stimulate viral sharing within and between online social networks.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".