Bibliographic record
Abstract
In the second half of the twentieth century, food scientists in North America and eventually across the globe focused their attention on the human sense of texture. Largely ignored by scientists before them, and seemingly without cultural precedent, corporate and public research scientists set out to define texture as a measurable phenomenon, to ascertain what was generally desirable to chew, and to operate this knowledge in service of the ongoing global industrialization of food production and retailing. Above all, texture was staked as the solution to overlapping crises in the global food system, promising the Global South abundant meat prepared cheaply from plants and offering a palatable alternative to excessive dietary fat in the Global North. Through an examination of their research methods, published ideas, and the new products that resulted, this dissertation explores the messy entanglement between food science and culinary culture. As scientists drew upon cultural intermediaries like home economists, marketers, and vegetarians to direct their research, so too did increasing scientific control over the physical features of processed foods shape broader cultural understandings about normative taste. While the desired outcomes for texture research have thus far resulted in spectacular market failure, this dissertation suggests how the core concepts of modern food science have succeeded nevertheless in becoming part of culinary discourse, offering a terminology and indeed a terrain for navigating contemporary global cultural exchanges.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".