You have to let go sometimes: advances in understanding goal disengagement
Bibliographic record
Abstract
While research on tenacious goal pursuit and persistence has evoked a myriad of research efforts, research on goal disengagement has rather been neglected and has been focusing mainly on positive consequences of individual differences in goal disengagement capacities. In recent years, however, research on goal disengagement has seen an upsurge in studies, specifically addressing the conceptualization of goal disengagement, the processes involved, and factors facilitating or undermining it. However, many questions remain unanswered or only partly answered providing numerous opportunities for further investigation. With this special issue of Motivation and Emotion, we aim to stimulate such progress in research on goal disengagement. To this end, this special issue includes empirical studies with cross-sectional, prospective, longitudinal, and experimental designs with a wide range of personal and experimentally induced goals as well as invited commentaries from scholars across different psychological sub disciplines. In this introductory essay, we provide a brief review of the current state of goal disengagement research. We also provide an overview about the contributions to this special issue with reflections related to the current state of research and areas where further advancement in conceptualization and empirical studies is needed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| 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".