Intergenerative Transdisciplinarity in “Glocal” Learning and Collaboration
Bibliographic record
Abstract
In this report, authors from North America, Africa, Europe, and Asia share commonalities and differencesin the lessons we are learning from COVID-19, especially about scholarship and collaboration. We represent different ages and disciplines hence our focus on intergenerational perspectives and transdisciplinary considerations. Our work is intergenerative{that is going "between to go beyond" by connecting creative sources of culture and focusing on the emergent, that is responding to changes in the context in which we work. And importantly in our view, we will point beyond whatever the next phase of COVID or even the next pandemic brings to a more hopeful, sustainable, and flourishing future, even as we face mounting social, health, and environmental challenges.
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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 | 0.034 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.071 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".