Unease and Nostalgia: The Marketing of "Pure Food" in the United States, 1890-1920
Bibliographic record
Abstract
This thesis examines the relationship between consumer culture and efforts to introduce legislation to regulate the production and labeling of American food products during the decades in the United States known as the Progressive Era.As food became increasingly mass-produced, consumer anxiety about contamination grew.Ultimately the federal government passed the Pure Food, Drink, and Drug Act in 1906 to ensure that Americanmade food items were produced in a hygienic manner and properly labeled.Using nostalgic modernism (characterized by scholars as a deeply-rooted impulse that causes Americans to simultaneously embrace and resist change) as a framework, my research demonstrates that the wide promotion of so-called "pure food" in the years both before and after the passing of this legislation reflects the pervasive ambivalence to modernity emblematic of this period.This project will demonstrate how food advertising reflected wider anxieties about a nation being reshaped by urbanization, industrialization, and corporate capitalism.I never thought I would be here.When I graduated from the University of Western Ontario many more years ago than I care to admit, I thought I was done with school.It is a cliché, but it is impossible to predict where life will take you.After a career as a journalist and writer, I decided I wanted a change and chose to pursue graduate studies in history.I enrolled in the MA program at Carleton under the supervision of Andrew Johnston, who, in a twist of fate, was once one of my professors at Western.In this sense, I feel like I have come full circle.I could not have researched and written this thesis without his support and guidance, and for this I am eternally grateful.He is one of the many wonderful faculty members in the history department at Carleton who have made my experience here such a fulfilling one.Acknowledgements must also be given to Susanne Klausen, whose insights and friendship have helped me immeasurably throughout the process of coursework, researching and writing a thesis, and applying to Ph.D programs.As well, in addition to their brilliant work and always helpful advice, Danielle Kinsey and Jennifer Evans have both shown me that it is possible to be a "cool academic" (I can't make it through anything without at least one Mean Girls reference).What is graduate school without the friends that you make along the way?Nick Hrynyk, it was fashion (of course) that brought us together that first week.I would be lost without you-you mean the world to me and I feel like I have known you for my entire life.I considered putting one of our many inside jokes in here, but I will be sincere for once and tell that I love you.Laura Lutes: we may disagree about many things, but we bonded over our shared neuroses and it was the start of a fabulous friendship.You will always be my favorite neighbor.
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".