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Record W7036690286

Crafting Cannabinoid Capitalism: Health, Sustainability, and Regeneration in the United States

2025· dissertation· en· W7036690286 on OpenAlexfundno aff

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2025
Typedissertation
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsEthnographyLegalizationIndigenousAmbivalenceNarrativeColonialismFrontierMateriality (auditing)MonopolizationPower (physics)Argument (complex analysis)
DOInot available

Abstract

fetched live from OpenAlex

This dissertation offers a critical exploration of cannabis legalization through an ethnographic study of small-scale "legacy" cannabis farmers in Humboldt County, California, as they navigate a complex transition from prohibition to commodity capitalism. I focus on their collective efforts to envision and practice “regenerative agriculture" as a response to both the historical injustices of prohibition and the compounding challenges of climate change. Drawing on history, STS, and the anthropology of food, agriculture, and medicine, I show how the logics of the war on drugs— rooted in carcerality, settler colonialism, and plantation agriculture—structurally and affectively persist in the socalled “post-prohibition” era, frustrating farmers’ efforts to resist monopolization and dispossession. Throughout, I attend to how the pervasive notions of “health,” “sustainability,” and “regeneration” are actively negotiated, modified, and put to use as material and symbolic tools in crafting medicinal, agricultural, and ecological futures. The Introduction weaves a tapestry of themes, histories, and theories that set the stage for the main ethnography. Through a blend of personal narrative, ethnographic vignette, and critical theory, it works to situate cannabis as a fluid and multifaceted object, highlighting people’s ambivalent hopes and cynicisms towards legalization. From alternative farming to molecularized biocapital, it articulates the intersecting influences of climate change, racial capitalism, and Indigenous sovereignties in ongoing projects to commercialize and legalize cannabis in a globally connected United States. Chapter One outlines my research methods and provides a social and narrative history of the study’s fieldsite, grappling with the anthropological complexities and complicities of studying working landscapes in a settler colonial “frontier ecology.” Chapter Two unpacks the shifting and embodied subjectivities of both farmers and workers as they reconfigure themselves in service of licensed production, highlighting sociocultural tensions and contradictions, the structural challenges of regenerative gardening, and the labor dynamics that shape these processes. Chapter Three analyzes how the inchoate and social nature of cannabis regulation both hinders and supports regenerative farming, emphasizing financial strain, and the ever-pervasive role that surveillance technologies are playing in cannabis governance. Chapter Four shifts to the harvest season, exploring farmers’ collective efforts to market their products through the concept of “drug terroir,” unpacking how their values and practices entangled with regional efforts to address wildfires and remediate leftover drug war infrastructures. Chapter Five moves off the farm and onto the topic of consumption as it historicizes the growing scientific literature about cannabis and pregnancy, demonstrating how carcerality continues to infiltrate maternal-fetal health science and conceptions of reproduction and health. The dissertation ultimately explores the ways in which American cannabis legalization often regenerates, rather than resolves, the legacies of prohibition and settler colonialism, while at the same time illuminating alternative and promising practices that might challenge these enduring forces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.332
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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