Toward an alternative dialogue between the social and natural sciences
Bibliographic record
Abstract
Interdisciplinary research within the field of sustainability studies often faces incompatible ontological assumptions deriving from natural and social sciences. The importance of this fact is often underrated and sometimes leads to the wrong strategies. We distinguish between two broad approaches in interdisciplinarity: unificationism and pluralism. Unificationism seeks unification and perceives disciplinary boundaries as conventional, representing no long-term obstacle to progress, whereas pluralism emphasizes more ephemeral and transient interdisciplinary connections and underscores the autonomy of the disciplines with respect to one another. Both approaches have their merits and pitfalls. Unification runs the risk of scientific imperialism, while pluralism can result in insurmountable barriers between disciplines. We made a comparison of eight distinct interdisciplinary attempts at integration of knowledge across social and natural sciences. The comparison was carried out as four pairwise comparisons: environmental economics versus ecological economics, environmental history versus historical ecology, resilience theory versus political ecology, and socio-biology versus actornetwork theory. We conclude by showing that none of these prominent eight interdisciplinary fields in and of itself manages to provide, in a satisfactory way, such an integrated understanding of sustainability. We argue for pluralism and advocate complex ways of articulating divergent ontological assumptions. This is not equivalent to pursuing knowledge unification either through scientific imperialism or by catering to the requirements of narrow practical utility. It means prioritizing interdisciplinary integration by simultaneously acknowledging the role of societal and natural factors in accounting for sustainability issues.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".