Metamotivational knowledge of the role of high-level and low-level construal in goal-relevant task performance.
Bibliographic record
Abstract
Metamotivation research suggests that people may be able to modulate their motivational states strategically to secure desired outcomes (Scholer & Miele, 2016). To regulate one's motivational states effectively, one must at minimum understand (a) which states are more or less beneficial for a given task and (b) how to instantiate these states. In the current article, we examine to what extent people understand the self-regulatory benefits of high-level versus low-level construal (i.e., motivational orientations toward abstract and essential vs. concrete and idiosyncratic features). Seven experiments revealed that participants can distinguish tasks that entail high-level versus low-level construal. Further, participants recognized the usefulness of preparatory exercises with which to instantiate high-level versus low-level construal for task performance, and this knowledge predicted behavioral choices. This research highlights novel insights that the metamotivational approach offers to research on construal level theory and, more broadly, to the study of self-regulation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".