Why the CCM Won’t Lose: The Roots of Single Party Dominance in Tanzania
Bibliographic record
Abstract
As many Sub-Saharan African countries enter their second decade of democratization, this paper provides a case study of the progress of democratization in one such country. Many predicted that multi-party democracy would infuse competition into the political system in these countries, adding an incentive for politicians to build strong bases of public support by becoming more responsive to majority interests. This paper addresses whether multi-party democracy has increased political competition in Tanzania, and hence whether democratization is indeed likely to increase the relevance of local concerns to national policy in Tanzania. In particular, this paper provides an empirical investigation of the factors contributing to single-party dominance and rural neglect in Tanzania. Despite the fact that Tanzania has had a multi-party democracy since 1995, the party which ruled during single-party rule, the Chama Cha Mapinduzi (CCM), won the vast majority of seats in the National Assembly in the first three multi-party elections. In order to understand the CCM’s grip on power, I analyze the results of a survey conducted amongst subsistence farmers in Tanzania in June 2008. This survey provides information on farmers ’ livelihood conditions, access to media, attitudes towards economic reform and political views, and hence provides insight into the preferences underlying voting behaviour and in turn into the factors contributing to the dominance of the CCM. The survey indicates overwhelming support for the CCM, despite policies which continue to prioritize sectors other than agriculture. I discuss the dominant reasons for CCM support, as well as the implications of single-party dominance for poverty reduction, political plurality and economic growth in the country. I conclude by discussing possible policy options for enhancing political participation amongst subsistence farmers. I am extremely grateful to George Kajembe, Steven Ngowi and Marco Njana of the Sokoine University of Agriculture for the assistance they provided with conducting the survey on which this paper is based. I also thank the University of Winnipeg for the Major Research Grant which financed this data collection. Any remaining errors are my own. 1 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".