Navigating human–plant reciprocity: Commercial harvesting by professionals of a medicinal plant fosters multi‐actor landscape management
Bibliographic record
Abstract
Abstract Studies of human–nature relationships increasingly recognise not only nature's contributions to people but also the positive contributions of human practices to ecosystems. The concept of reciprocal contributions emphasises positive human–nature relationships. But trade‐offs between natural elements implies that human favouring of one element (e.g. via the protection of its habitat) can be detrimental to others. Discussing the concept of reciprocal contributions encourages us to rethink human management of landscape by shifting from a primary focus on instrumental values associated with plant extraction, to relational values related to the multiple interests of human and non‐human actors. To study how relational values are integrated into the configuration of multifunctional landscapes, we focused on professional harvesters of Arnica montana . We asked what role professional harvesters play in the stewardship of their harvesting sites through reciprocal relations with plants, landscapes and other actors to shape the future of plant and landscape sustainability. We show that even though professional harvesters live far from their harvesting sites, they develop both a strong attachment to them, and an experience‐based ecological knowledge of the relationships between arnica and other plant species and the environment. This attachment and experience‐based knowledge provide harvesters with legitimacy in the eyes of other actors (e.g. cattle farmers, managers of natural areas, pharmaceutical and cosmetic laboratories) and allow them to play the role of mediator between these other actors and the harvested plant in order to influence the management of the environment—for example by burning, mowing or grazing. This creates a reciprocal benefit with this particular species, but also with other co‐occurring species. Integrating the interests of the harvesters with those of other stakeholders requires negotiation and the search for synergies between values. Synthesis and applications . Within the framework of ‘reciprocal contributions’, we argue that human engagement in reciprocal relations with specific species is read as a form of care that privileges the maintenance of certain lives over others; trade‐offs between plants but also between plants, animals, landscape and humans have to be incorporated in the theoretical framework. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.000 |
| 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".