Waves and legacies: the making of an investment frontier in Niassa, Mozambique
Bibliographic record
Abstract
The literature on land-use frontiers has overwhelmingly focused on active frontiers of expansion. We focus on an emerging frontier. We studied the decisions, narratives, and practices of the actors driving land-use change in Niassa, Mozambique. Based on ethnographic research carried out between early 2017 and late 2018 among investors engaged in commercial agriculture and plantation forestry, we show how successive waves of actors with different backgrounds, motives, and business practices arrived in Niassa and attempted to establish farms or plantations yet repeatedly failed and left, or remained but continued to struggle. We show how even though waves come and go, they do leave sediments behind, legacies that over time add up to overcome the various constraints that investors face and gradually form the conditions for a frontier to emerge. We argue that the build-up of these legacies, particularly after the end of the civil war in 1992, has given rise to a new wave, which is qualitatively different from the previous ones in the sense that the actors did not arrive from elsewhere but were already present in Niassa. This wave thus emerges from within the region, building on the legacies of previous waves, indicating that over time endogenous processes may replace externally driven waves. We contribute to frontier theory by arguing that waves and legacies shape emerging frontiers through their dynamic interaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".