Social identification with animals: Unpacking our psychological connection with other animals.
Bibliographic record
Abstract
Our relations with other animals are ubiquitous in human life, yet the psychological structure of our connection with animals is just beginning to receive empirical attention. Drawing on theories of social identification and intergroup relations, we investigate the various ways that people identify with animals. Across 7 studies, we introduce the Identification with Animals Measure (IWAM) and uncover 3 dimensions by which humans identify with animals as a group: solidarity with animals, animal pride, and human-animal similarity. First, we establish the reliability, factorial structure, and predictive validity of the 3-factor IWAM. Next, we find that these factors predict a distinct set of attitudes and behaviors toward animals. Solidarity with animals is defined by feeling connected to other animals and is associated with more contact with animals (i.e., pets) and a greater desire to help animals and to engage in collective actions on their behalf, even if this implies withdrawing privileges to humans. Human-animal similarity is defined by the perception that animals share similarities with humans; this dimension is associated with increased moral concern for their welfare and a greater attribution of typically human traits to other animals. Finally, animal pride is defined by a direct recognition and positive endorsement of the social category that includes all animals, and is associated with viewing humans as more animal-like, and with more competitive and instrumental intergroup relations. The findings confirm that identification with animals is a multidimensional construct that is colored by the unique and complex nature of our relations with nonhuman animals. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".