Should We Approach Approach and Avoid Avoidance? An Inquiry from Different Levels
Bibliographic record
Abstract
Approach motivation (striving for desired end-states, eagerly focusing on where one wants to be) is often held up as the best type of motivation: It feels good and is associated with many positive outcomes. Indeed, a common perception is that regulation in terms of approach motivation is almost always better than regulation in terms of avoidance motivation. However, as we discuss, this conclusion is worthy of a deeper look. We consider how approach and avoidance motivation manifest at different levels in a self-regulatory hierarchy and how this can help us understand the upsides and downsides of both approach and avoidance motivation. In other words, approach motivation is not always beneficial and avoidance motivation is not always problematic. Understanding these trade-offs involves a consideration of which level in the hierarchy approach or avoidance is manifested, what types of outcomes are being examined (the experience of regulation vs. performance), and how the approach or avoidance regulation fits or does not fit with an individual’s broad concerns or specific situational demands. Furthermore, a hierarchical approach helps make sense of behaviors that reflect simultaneous approach and avoidance tendencies, such as tactical approach to remove (avoid) a threat, providing a dynamic and nuanced view of motivation.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".