Should We Approach Approach and Avoid Avoidance? An Inquiry from Different Levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it