Being Perceived as a Vital Force or a Burden: The Social Utility-Based Acceptance/Rejection (SUBAR) Model
Bibliographic record
Abstract
This paper proposes a new theoretical model to explain the acceptance/rejection of agents (co-workers) and various social groups (people with mental disorders or disabilities, the elderly, the unemployed/poor, ethnic minorities) in a given social system: the social utility-based acceptance/rejection (SUBAR) Model. Based on a social utility approach, it is proposed that human social cognition evaluates and reacts to agents/groups in a social system on the basis of the perceived strengths and significant contributions they bring to the system (upward forces; e.g., skills, resources, willingness) and the perceived weaknesses that may harm the system (downward forces; e.g., use of social benefits, dependence). While the perception of upward forces for the system (i.e., vital forces) is accompanied by acceptance (positive attitudes and behaviors), the perception of downward forces (i.e., burdens on the system) promotes rejection (negative attitudes and behaviors). The combination of the two indicators predicts that low vital forces/high burden targets will be the most rejected and high vital forces/low burden targets will be the most accepted. The high burden/high vital forces and low vital forces/low burden targets should be evaluated at an intermediate level between the other two. This naive calculation of the forces exerted by agents/groups in a social system is moderated by various variables (scarcity of economic resources, values) and responds to a functional attempt to regulate individual and collective interests, themselves dependent on the efficiency of given systems. Finally, the relationship of the SUBAR model to other relevant theories will also be discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".