Prepare for Trouble and Make It Double: The Power Motive Predicts Pokémon Choices Based on Apparent Strength
Bibliographic record
Abstract
Two social motives are distinguished by Motive Disposition Theory: affiliation and power. Motives orient, select and energize our behaviour, suggesting that the choices of power-motivated individuals should be guided by power cues, such as the appearance of strength in a game character or avatar. In study 1 we demonstrate that participants were more likely to pick strong-looking Pokémon for a fight and cute Pokémon as a companion. In addition, we show that even when considering these contexts, the power motive predicts preferences for a powerful appearance, whereas affiliation does not. In study 2 we replicate the study 1 findings and distinguish between two ways to enact the power motive (prosocial and dominant power). We demonstrate that the dominance, but not the prosociality, facet drives the preference for strong-looking Pokémon. Our findings suggest that the need to influence others—the power motive—drives the choice for battle companions who symbolize strength.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".