Adaptation Revisited: An Invitation to Dialogue
Bibliographic record
Abstract
Within this opportunity to dialogue in commentary exchange about a previously conceived adaptation model, published in the Journal of Clinical Sport Psychology, we revisit the utility of our model (Schinke et al., 2012a) and consider Tamminen and Crocker’s (2014) critique of our earlier writing. We also elaborate on emotion and emotion regulation through explaining hedonistic and instrumental motives to regulate emotions. We draw on research from general and sport psychology to examine emotion regulation (Gross, 2010). We argue that when investigating emotion, or any topic in psychology, the process of drawing from knowledge in a different area of the discipline can be useful, especially if the existing knowledge base in that area is already well developed. In particular, we draw on research using an evolutionary perspective (Nesse & Ellsworth, 2009). Accounting for these issues, we clarify the adaptation framework, expand it, and arguably offer a model that has greater utility for use with athletes in relation to training and competition cycles and progressions throughout their career. We also clarify for the readership places of misinterpretation by the commentary authors, and perhaps, why these have resulted.
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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 | 0.045 | 0.264 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.063 | 0.091 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".