Bibliographic record
Abstract
Imagine yourself walking to the gym in the rain after a long hard day at work. Picture yourself lifting heavy weights, even though you would prefer sitting on the sofa watching your favorite baseball team win a playoff match. Envision a marathon runner who keeps pushing herself during the final miles of a run, trying to override her thoughts of quitting the straining competition. These are just some sports-related examples of situations during which self-control processes enable us to keep striving for a desirable goal and suppress po-tentially tempting action alternatives. In general, “self-control refers to the capacity for alter-ing one’s own responses, especially to bring them into line with standards such as ideals, values, morals, and social expectations, and to support the pursuit of long-term goals” (Baumeister, Vohs, & Tice, 2007, p. 351) . However, self-control is not always applied effec-tively as, for instance, evidenced by the large number of gym dropouts every year (e.g., Eng-lert & Rummel, 2016). In this chapter, we will discuss empirical findings that highlight the importance of self-control for sports-related performance and we will introduce the theoretical accounts that try to explain why self-control sometimes appears to fail. Finally, we will discuss open research questions in order to improve our understanding of how self-control operates and why it is not applied at all times.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".