Bibliographic record
Abstract
The Earth to Tables Legacies Project emerged in 2015, growing out of personal relationships, but also built on a long trajectory of participatory research, multimedia arts production and popular education. We created an intergenerational and intercultural exchange of food activists working for food justice and food sovereignty with the initial goal of producing a feature length documentary. However, the project evolved over five years to culminate in a multimedia educational package with 10 short videos and 11 photo essays, all accompanied by facilitator’s guides. A web series on the pandemic is in production and a forthcoming book is to be published in 2021. The intergenerational production team included Deborah Barndt (co-director and co-editor), Lauren Baker (co-editor) and Alexandra Gelis (co-director). In this ‘report from the field,’ the two co-directors Alexandra and Deborah look back on the process of co-producing the visual materials for the interactive website and look forward to its potential use in university classes, schools, and social and environmental justice organizations. Parts of the essay include our zoom dialogue as we revisit our process over the past five years and try to elucidate our way of working, while reflecting on the challenges of the collaborative production and use of multimedia educational tools. Note that this essay utilizes the same kind of text with hyperlinks that are featured in our website and book. The reader is encouraged to click on the links to learn more about the people and their practices as well as the concept of a non-linear multimedia educational tool and process.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.055 | 0.034 |
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".