Self-regulating the effortful “social dos”.
Bibliographic record
Abstract
In the current research, we explored differences in the self-regulation of the personal dos (i.e., engaging in active and effortful behaviors that benefit the self) and in the self-regulation of the social dos (engaging in those same effortful behaviors to benefit someone else). In 6 studies, we examined whether the same trait self-control abilities that predict task persistence on personal dos would also predict task persistence on social dos. That is, would the same behavior, such as persisting through a tedious and attentionally demanding task, show different associations with trait self-control when it is framed as benefitting the self versus someone else? In Studies 1-3, we directly compared the personal and social dos and found that trait self-control predicted self-reported and behavioral personal dos but not social dos, even when the behaviors were identical and when the incentives were matched. Instead, trait agreeableness--a trait linked to successful self-regulation within the social domain--predicted the social dos. Trait self-control did not predict the social dos even when task difficulty increased (Study 4), but it did predict the social don'ts, consistent with past research (Studies 5-6). The current studies provide support for the importance of distinguishing different domains of self-regulated behaviors and suggest that social dos can be successfully performed through routes other than traditional self-control abilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".