"Doing more good": Exploring the multidisciplinary landscape of regeneration as a boundary concept for paradigm change
Bibliographic record
Abstract
The concept of regeneration is gaining traction across diverse disciplines, from agriculture and engineering to business and the social sciences. More than just a buzzword, regeneration is emerging as a pivotal boundary object in a paradigm shift that is redefining design principles and transforming humanity’s relationship with the environment. This narrative review explores regeneration’s journey from its literal origins in biology and engineering to its metaphorical applications in areas such as regenerative economics, agriculture, and culture. We argue that regeneration’s conceptual fluidity allows it to adapt and resonate across domains while maintaining a core ethos of holistic, proactive care and stewardship. Central to regeneration is the notion of generativity—a principle that champions giving back more than what is taken, fostering reciprocity, and co-creating a thriving world for all. As regeneration gains prominence, there are risks that it will be misappropriated or diluted by greenwashers; however, its power lies in its ability to facilitate interdisciplinary dialogue and place-based solutions. Rather than limiting regeneration through strict definitions, we propose nurturing its development through collaborative social agreements like covenants and treaties that enshrine its core tenets of generativity, diversity, and care. We believe that regeneration’s emergence across disciplines heralds a new era of environmental thought and action—one where humanity moves beyond harm reduction to actively healing and enriching the social and ecological systems that we are part of. This review provides a foundation for scholars and practitioners to engage critically with regeneration and collaborate across boundaries to address pressing socio-ecological 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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| 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".