Power Struggles in Overlapping Social Fields: Explaining Investors Related Land Conflicts in Tanzania
Bibliographic record
Abstract
This paper contributes to the scholarship on the local impacts of land investments by analysing how the expanding presence of investors in a Tanzanian village has led to land conflicts. Moreover, I seek to understand why two land conflicts have unfolded in different ways in the same Tanzanian village, and why the actors involved have chosen to act differently in similar and simultaneous situations. Building up on the emerging scholarship that looks at the local impacts of land deals, I argue for the utility of thinking local reactions to land deals as not only as historically embedded in sociopolitical contexts, but also as relational processes that are continuously actualized. Theoretically, I claim that using Bourdieu’s concept of field allows to better capture power flux, and to delve into the several micro-power dynamics that coexist within local political configurations. I make three main contributions. First, I draw attention to the importance of considering investments’ direct but also indirect impacts on existing local political dynamics and land rights to understand local reactions. Investors’ presence may have adverse collateral consequences for the most vulnerable local population, accentuating already existing differentiation dynamics, thereby further marginalizing them. Second, I demonstrate that actors’ role and position cannot be assumed to be constant in time and space: they depend on contingent configurations of power that constantly (re)define political fields. Finally, I highlight the importance of acknowledging the shaping nature of contingencies, which alter and influence in unexpected ways social fields, thereby constraining actors’ choices and power in addition to significantly influencing trajectories of land conflicts.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| 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".