Effects of Social Inhibition on Selection of Artifact Capabilities.
Bibliographic record
Abstract
Tool or artifact use is prevalent in the human race. Over time humans learn, evolve and modify these capabilities in order to achieve their goals facilitating their adaption in an ever changing environment. Once an artifact capability is learned however, humans are often faced with the decision making process of which capabilities to apply at any given time. These decisions are not only affected by their internal states but also the social environment in which they operate. In this study we present a computational multi-agent simulation model that investigates how social inhibition affects the artifact capability-selection process. Inspired by models of social inhibition in the field of specialization, we demonstrate that functioning in a social environment often leads to the inability to select and perform the capabilities that we inherently desire. The model also tests the effects of demand on the capability selection process. Experiments conducted demonstrate that at a group level social inhibition may contribute to a decline in the performance of the group. It is also observed that group performance increases alongside demand suggesting that higher demand may reduce the effects of so-
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".