Bibliographic record
Abstract
Since March 2020, the COVID-19 pandemic propelled the “stay-at-home” policy worldwide under public health uncertainty, resulting in increased individualization, as well as an increased reliance or dependency on digital communication technology. Based on a review of existing literature alongside a reflection on personal fieldwork experiences, we aim to: (1) describe major elements of agroecological pedagogy, (2) explore adaptation pathways to combine digitalization and participatory action-learning, and (3) briefly discuss opportunities and challenges for agroecologists beyond COVID-19. Agroecological pedagogy is deeply embedded in the praxis, the scientific knowledge and ways of knowing (academic or not), and in the politics and agency of food movements. In line with Freire's liberation pedagogy, seeing what already exists (e.g., in: ecosystems, home-gardens, fields, farms, and watersheds) through participation and volunteering. Alongside a critical analysis to explain and explore certain phenomena, causes and consequences will likely result in the act leading to the implementation of transformative practices and novel designs that improve the state of any situation being addressed. Participatory action research/learning methods are strategic in agroecological pedagogy. Overall, the lockdown period led to increased societal digitalization of human interactions. During lockdown, however, the implementation of strategies for remote agroecology participatory action-learning were hampered, but not vanquished. Key changes to agroecology education projects “before” and “during” lockdown include an increased reliance on digital and remote strategies. Creative adaptations in the virtual classrooms were designed to nurture, deepen, and foster alternatives in favor of diverse knowledges and ways of knowing for food system transformations.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".