Institutional Translation Gone Wrong: The Case of<i>Villages for Africa</i>in Rural Tanzania
Bibliographic record
Abstract
Why do ideas that have been successfully moved across highly different contexts subsequently fail? To answer this question, we use longitudinal data on the Dutch organization Villages for Africa that introduced ‘macro-credit’ loans to rural Tanzanians that would enable them to establish their own village enterprises. Only two years after the seemingly successful implementation of the idea, it collapsed. Our findings allow us to make two key contributions. First, we provide a process model of high-distance translation that shows how proponents can strategically introduce an idea across highly different contexts by ‘culturally detaching’ it from its institutional origins, leading to the idea being ‘culturally assimilated’ into the recipient context. But, although cultural detachment and cultural assimilation indicate the successful translation of an idea, the means of doing so can later prompt its rejection. We call this the reactance effect of translations across highly different contexts. Second, we showcase the role of history for translation theory more generally. History – particularly the historical relationship between the socio-cultural categories of the mzungu (Swahili: “foreigner”) and the villagers –influenced the way in which the macro-credit idea could be introduced to villagers and played a key role in its subsequent rejection.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".