Development porn? Child sponsorship advertisements in the 1970s
Bibliographic record
Abstract
Purpose Child sponsorship programs have been accused of representing children in the developing world in a manner described as “development porn”. The purpose of this paper is to take an historical approach to investigating the use of advertising techniques by Plan Canada, a subsidiary of one of the oldest and largest child sponsorship‐based non‐governmental development agencies, Plan International, during the 1970s. This time period represents an important era in international development and a time of significant change in the charitable giving and advertising industries in Canada. Design/methodology/approach The authors conduct a content analysis on an archival collection of 468 print advertisements from the 1970s. Findings A description of the “typical” Plan Canada fund‐raising ad is presented and shown to be different, in several aspects, from other advertisements of the time period. It was determined that Plan Canada's advertisement did not cross the delicate line between showing the hardship and realities of life in the developing world for these children and what became known as “development porn”. Originality/value There has been little previous research which focuses specifically on the design of charity advertisements. This paper presents a historically contextualized description of such ads, providing a baseline for further research. It also raises important questions regarding the portrayal of the “other” in marketing communications and the extent to which aid agencies must go to attract the attention of potential donors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".