Bibliographic record
Abstract
Non-governmental organizations (NGOs) and non-profit organizations (NPOs) seek creative ways to raise awareness about issues and connect with donors to raise the resources required to address the needs of the communities they work with. This paper highlights ethical challenges and lessons learned in using images and storytelling in the development sector. This article resulted from a capacity-building event on the ethical use of images from development projects. It draws from the event itself, which was organized by the Manitoba Council for International Cooperation (MCIC) in 2022, and from conversations leading up to and following the event between the author, guest speakers, and additional stakeholders. The workshop was led by two representatives from MCIC member organizations. These two guest speakers shared experiences of using images and storytelling in their work in the Global South. Based on these conversations, this article highlights unequal power relations around the use of images from development projects, emphasizing organizations’ ethical challenges and how storytelling is (re)constructed amidst competing needs and expectations. The paper contributes to the ongoing discourse on ethical storytelling in fundraising and the need for decolonial storytelling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".