Examining interactions between adventure seeking and states of the four channel flow model
Bibliographic record
Abstract
This study evaluates the relationships between states of the four channel flow model and adventure seeking traits among Whitewater kayakers using a modified Experience Sampling Method. Study hypotheses were concerned with determining whether the interaction between adventure seeking and the four channel flow model predicts differences in dimensions of subjective experience. Questionnaires were administered on‐site to 52 Whitewater kayakers on the Cheat River in West Virginia at eight sites varying in river difficulty (Class I‐V). Data were analyzed at the level of single experience measurements (n = 409 experience observations) rather than per respondent. Statistical analyses (using principal axis factoring and hierarchical linear modelling) confirmed a three dimensional structure of flow indicators, and that the interactions of adventure seeking and the channels of the flow model were significant predictors of an Intrinsic Freedom dimension. Although the adventure seeking trait was a significant predictor of the Affect and Activation dimension, this dimension and the Cognitive Control dimension were not significantly predicted by interactions with channels of the flow model. The significant interaction between the flow state and adventure seeking trait in predicting the Intrinsic Freedom dimension suggests that higher adventure seeking, coupled with entering the flow state, enhances the intrinsic nature of the subjective experience in the Cheat Canyon. Implications of this interaction include a focus on programming for opportunities that inspire intrinsic freedom.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".