We Move Up Levels Together: Dignity, Transformative Marketing, and the Repurposing of Racial Capitalism
Bibliographic record
Abstract
Indigenous Bolivians, especially women, are climbing the ranks of global multilevel marketing (MLM) companies like Herbalife, Omnilife, and Hinode, seeking to join Bolivia’s purportedly rising Indigenous middle class. Through MLMs, Indigenous direct sales distributors pursue a dignified life materialized in better homes, smart dressing, international travel, and the respect they receive at recruitment events. In their recruitment and sales pitches to potential buyers and downline vendors, Indigenous distributors fashion testimonials about their successes that explicitly critique existing avenues of class mobility and their racialization in two ways. First, these testimonials counter the skepticism that multilevel marketing companies face by citing a litany of false promises offered by higher education, salaried employment, and public sector jobs—avenues long heralded as the stepping stones to entwined racial and class mobility in Bolivia. They further voice their frustrations with perceived status hierarchies and organizational barriers among Indigenous merchants, highlighting their own sense of alienation from the connections and protections that have enabled the financial success of other Indigenous entrepreneurs. Second, while lodging these critiques, distributors repurpose racialization toward their own recruitment ends. As MLM distributors pursue their visions of the good life, the testimonials that Indigenous MLM recruiters craft to enable their ascent expose, rely on, and rework the bounds of racial capitalism. Ultimately, their critiques reveal how the racial partitioning that enables capitalist extraction operates through the work of direct sales.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.083 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".