Bibliographic record
Abstract
The concept of relational values, which came to prominence in the 2010s, proposes a way of attributing value to nature beyond traditional notions of instrumental and intrinsic valuations. This third semantic domain of value has attracted significant attention within academia, and at academia's interface with policy and management. Consequently, this attention has led to research that analytically employs the concept to help name the specific relational values that are relevant to certain individuals and communities. This double-naming process, i.e., relational values as an overarching concept and the naming of specific relational values within it, yields substantial new knowledge regarding human-nature relationships, which, according to Foucauldian theory, means that this knowledge is exerting power. In this paper, I propose the term relational-values apparatus for the assemblage of heterogeneous entities and associations that produce knowledge regarding human-nature relationships utilizing the concept of relational values. By naming and explaining this power-exerting apparatus, the concept of relational values becomes more transparent and useful as a tool for managing complex social-ecological systems.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".