“Dream brokers” and the moral economy of frontier investments
Bibliographic record
Abstract
• Investments in land-use frontiers are morally fraught due to their social and ecological impacts. • Frontier investors ease these moral tensions by mobilizing narratives about frontier land-use change and their role in it. • Narratives downplay moral costs & upsell benefits of investments, while portraying investors as agents of positive change. • Brokers who help investors navigate material dimensions of frontiers also actively shape and disseminate these narratives. • That discursive work is an important way in which brokers contribute to bringing about the “dream” of frontier development. Agricultural frontiers continue to expand rapidly in multiple parts of the world, with massive social and ecological consequences. As awareness of these issues rises, so does societal pressure on frontier investors, who find themselves under increased scrutiny from civil society and environmental groups. In this study, we argue that frontier actors, in response to these pressures, deploy narratives that seek to resolve moral tensions around their activities in an attempt both to lessen exposure to criticism and to appease their own moral insecurities. Through a re-analysis of interviews conducted between 2013 and 2018 in two land-use frontier settings, the Gran Chaco region in South America and Niassa province in Mozambique, we explore the narratives used by frontier investors to frame and justify their actions as rightful and appropriate. We also highlight the important role played in this process by brokers who, in addition to facilitating these investments materially, operate at the discursive level to fashion these narratives into a positive, socially acceptable vision of frontier development. In disseminating the carefully curated “dream” of a morally just frontier, brokers pre-empt critiques of frontier investments and help bring about the future they want.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".