Sustainability Agency - From Individuals as Change Agents Toward Multi-Species Perspectives
Bibliographic record
Abstract
• Symposium introduction, Presented by Professor Satu Teerikangas, University of Turku & University College London • Presentation 1: Homo Sustinens - A Future Philosophical Perspective on Agency, Presented by Associate Professor Efrosyni Konstantinou, University College London • Presentation 2: Entrepreneurial Sustainability Agency - Insights from the Circular Economy Transition, Presented by Professor Hanna Lehtimäki, post-doctoral researchers Dr. Subhanjan Sengupta and Dr. Ville-Veikko Piispanen, and Associate Professor Kaisa Henttonen, University of Eastern Finland • Presentation 3: Reflections on Middle Managers’ Sustainability Agency, Presented by Associate Professor Gustavo Birollo, Laval University, Québec, Canada • Presentation 4: Forest Workers at The Crossroads - Conflicting Values in Finland’s Forestry Transition, Presented by Doctoral researcher Julia Autio, Associate Professor Jan Hermes & Associate Professor Ilkka Ojansivu, University of Oulu • Presentation 5: Bringing the Rest of Nature Back in - Multispecies Perspectives on Agency, presented by post-doctoral researcher Kari Jalonen Demos Helsinki and post-doctoral researcher Valtteri Aaltonen, University of Helsinki, Finland • Commentary offered by Professor Sally Russell, University of Leeds • Q&A chaired by Associate Professor Tiina Onkila, University of Jyväskylä
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| 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".