Plantness, Animalness, and Humanness: plant placement within animacy and adjacent scales
Bibliographic record
Abstract
Abstract Animacy is an important framework through which humans view and categorize the world, but many objects do not easily fit within this scale. Plants are unique because they are very familiar to humans, yet the features and traits relevant for placement within the animacy scale are generally poorly understood by the public. Animacy occurs at three levels, with the inherent attributes of the object (biology), how they are perceived (cognition), and how they are expressed in languages (linguistics). Animacy is dependent on qualification and perception as alive, mobile, and intentional. In the absence of visible movement, classification is dependent on featural attributes indicating mobility or placement in a group recognized as animate (animalness). Plants have complicated bodies whose forms and structures are frequently clear representations of their life history and function (plantness), more than many animals, yet these signs of movement and activity are rarely recognized. The animacy scale may be more closely based on human similarity (humanness) with humans as the peak of life, mobility, and intentionality. As humans, we can have an anthropocentric viewpoint, rendering plants as scenery or utility, or use anthropomorphic interaction to better understand and recognize the dynamic lives of plants. The goal of this review is to compare the current evidence on the placement of plants within animacy and adjacent scales with the biology and habit of land plants, to better understand human perception and behaviour, and work with this process to educate and inform people about the complex lives of plants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".