Dynamics of affordance actualization
Bibliographic record
Abstract
The actualization of affordances can often be accomplished in\nnumerous, equifinal ways. For instance, an individual could\ndiscard an item in a rubbish bin by walking over and dropping\nit, or by throwing it from a distance. The aim of the current\nstudy was to investigate the behavioral dynamics associated\nwith such metastability using a ball-to-bin transportation task.\nUsing time-interval between sequential ball-presentation as a\ncontrol parameter, participants transported balls from a\npickup location to a drop-off bin 9m away. A high degree of\nvariability in task-actualization was expected and found, and\nthe Cusp Catatrophe model was used to understand how this\nbehavioral variability emerged as a function of hard (time\ninterval) and soft (e.g. motivation) task dynamic constraints.\nSimulations demonstrated that this two parameter state\nmanifold could capture the wide range of participant\nbehaviors, and explain how these behaviors naturally emerge\nin an under-constrained task context.
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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 | 0.014 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".