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
Abstract Modeling and imagery are distinct but related psychological skills. However, despite sharing similar cognitive processes, they have traditionally been investigated separately. While modeling has shown similar psychological and physical performance benefits as imagery, it remains an understudied technique within applied sport psychology. Social cognitive and direct perception approaches remain often-used explanations for the effectiveness of modeling on skill acquisition; however, emergent neuropsychological explanations provide evidence to support these earlier theories and a link to the imagery literature. With advances in technology and the development of applied frameworks, there is renewed interest in exploring modeling effects and how they parallel imagery use in applied settings. Specifically, modeling research has expanded beyond controlled laboratory settings to explore the effect of various theoretical models on motor performance and related cognitions within practice and competitive settings. The emergence of affordable video editing technology makes it easy for coaches and athletes to incorporate modeling into practice. The accessibility of video technology has sparked applied research on how various forms of modeling influence motor performance and cognitions, such as confidence and motivation. These applied investigations demonstrate the complementary nature of modeling and imagery in enhancing sport performance and skill acquisition, while highlighting the challenges in separating modeling and imagery effects. Both literatures offer possibilities for new methodological approaches and directions for studying these psychological skills in tandem as well as independently. Thus, there is much that imagery and modeling researchers can learn from each other in sport and other performance settings.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| 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 it