Bibliographic record
Abstract
Where does our food come from? Whose hands have planted, cultivated, picked, packed, processed, transported, scanned, sold, sliced, and cooked it? What production practices have transformed it from seed to fruit, from fresh to processed form? Who decides what is grown and how? What are the effects of those decisions on our health and the health of the planet? Tangled Routes tackles these fascinating questions and demystifies globalization by tracing the long journey of a corporate tomato from a Mexican field to a Canadian fast-food restaurant. Through an interdisciplinary lens, Deborah Barndt examines the dynamic relationships between production and consumption, work and technology, biodiversity and cultural diversity, and health and environment. A globalization-from-above perspective is reflected in the corporate agendas of a Mexican agribusiness, the U.S.-based McDonald's chain, and Canadian-based Loblaws supermarkets. The women workers on the front line of these businesses offer a humanized globalization-from-below perspective, while yet another "globalization" is revealed through examples of resistance and local alternatives. This revised and updated edition highlights developments since the turn of the millennium, in particular the deepening economic integration of the NAFTA countries as well as the growing questioning of NAFTA's consequences and the crafting of alternatives built on foundations of sustainability and justice.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.150 | 0.040 |
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".