Collaborative Work Using Give and Take Passing Protocols
Bibliographic record
Abstract
To effectively design computer simulations of shared environments, an understanding is needed of the basic informational requirements and underlying movement patterns generated by two people collaborating in these environments. Results from this study indicate that when passing objects in a natural environment, the fundamental movement patterns seen during simple grasping tasks are altered to accommodate the collaborative nature and social constraints of the task. When giving objects, although subjects reach further they reach more quickly than when the objects are taken. This result may indicate a social consideration taken by the passer to move quickly and efficiently over a long distance to transfer the object to the receiver. Thus, the receiver does not have to travel as far or as fast to receive the given object. However, when objects are taken, passers move more slowly and lift the objects higher. This result may indicate that the passer times their movement so that they are not waiting at the end of their movement for the receiver to reach the target. Thus, although the result of the task is the same (the receiver obtains the object), the underlying movement patterns differ with the goal and social constraints of the movement. These results may be used to develop predictive algorithms when designing virtual and augmented environments. Future experiments will concentrate on the nature of visual and haptic information required for both the passer and receiver to effectively perform a passing task in an augmented environment.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".