Bibliographic record
Abstract
As a focus of research and professional practice – distinctive from the study of disaster management or routine philanthropy – disaster philanthropy is still nascent. Guiding theories of giving, distributing and regulating disaster philanthropy are fledgling, if they exist at all. Nevertheless, the preceding chapters reveal some clear recurring themes that can serve as a basis for deeper exploration and theory development, including evolving definitions of key terms, process and governance, the interface of philanthropy and the state, intra- and intersectoral collaboration and coordination, and distribution of resources pre-versus post-disaster. Weaving through these broad themes are common threads of donor expectations, communications and time. This final chapter considers and integrates these themes and threads, suggesting reflections and questions for future research. Recurring themes Definitions A key theme throughout the volume is that of definitions and shared conceptual understandings, which give rise to at least two related issues. The first is how ‘disaster’ is defined. When an event qualifies as a ‘disaster’ is touched on in several chapters. Conway notes that there are 128 definitions of disaster in the literature; Leat highlights changing notions of disaster over time, as well as the public recognition of disaster as a political statement. The chapters use examples of events already categorised and labelled as ‘disasters’ yet there are clearly events that were a disaster for some people but not designated as such. The second issue is whether we now see more disasters because we generally expect to live in a humanly controlled and controllable world. Disaster is abnormal, extraordinary. However, in the past, disaster may have been accepted as a normal part of life, while today disasters are perceived to be more disruptive and extraordinary because they are not subject to normal human controls. In addition, the 24-hour news cycle brings disasters into our lives with immediacy and direct connection to personal suffering. How strong is the link between definition of an event as a disaster and the nature and scale of the philanthropic response? Scaife discusses the importance of the media in defining and communicating disaster for fundraising purposes and the complexity of this relationship: perhaps counter intuitively, more coverage does not always mean more money raised.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.118 | 0.045 |
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".