Bibliographic record
Abstract
First paragraph: If there is one academic book that will make one “hungry for change,” it is Earth to Tables Legacies by Deborah Barndt, Lauren E. Baker, and Alexandra Gelis. This book is full of colors and offers a novel format, as it is a multimedia celebration of stories and visions for a better planet through food systems transformation. Also novel about this book is that it provides resources for the readers to help facilitate dialogue and includes notes on how its readers can participate in an interactive website with videos and photo-essays from diverse “legacies collaborators.” While some of the contents are harrowing, covering issues such as Indigenous residential schools as well as corporate concentration and racism, the approach Barndt, Baker, and Gelis use to bring the reader in is healing, a clear homage to the Indigenous teaching that food is medicine. A foreword by Indigenous scholar Robin Wall Kimmerer emphasizes the transformative power of food, the importance of reciprocity, and honors the Haudenosaunee “Dish with One Spoon” treaty. This particular treaty sets the context for where this project was originally seeded, in Tkaronto/Toronto, Ontario. It is a reminder that the metaphorical “dish” (earth) is meant to be shared and that we all use one “spoon,” and there is a responsibility to ensure that there is enough for everyone. As Kimmerer writes in the foreword, “there is only one dish and only one spoon, the same size for everyone. It is a statement about making justice” (p. xii). . . .
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".