Social fields and natural systems: integrating knowledge about society and nature
Bibliographic record
Abstract
Sustainability science is a wide and integrative scientific field. It embraces both complementary and contradictory approaches and perspectives for dealing with newer sustainability challenges in the context of old and persistent social problems. In this article we suggest a combined approach called social fields and natural systems. It builds on field theory and systems thinking and can assist sustainability scientists and others in integrating the best available knowledge from the natural sciences with that from the social sciences. The approach is preferable, we argue, to the various scientific efforts to integrate theories and frameworks that are rooted in incompatible ontologies and epistemologies. In that respect, this article is a critique of approaches that take the integration of the social and natural sciences for granted. At the same time it is an attempt to build a promising alternative. The theoretical and methodological pluralism that we suggest here, holistic pluralism, is one way to overcome incommensurability between the natural and the social sciences while avoiding functionalism, technological and environmental determinism, and over-reliance on rational choice theory. In addition, it is a basis for generating better understandings and problem solving capacity for sustainability challenges.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".