Bibliographic record
Abstract
Being able to work effectively in any collaborative venture is beneficial for everyone who is involved in the undertaking. In order for a collaborative venture to be effective, it must allow a wide range of people possessing different expertise to interact in a seamless manner. Thus, it is of utmost importance to investigate the underlying strategy that permits people with different expertise to work together and accomplish the task at hand. We began our investigation by observing, from previous research, that the human visual system comprises of two streams; each stream possessing complementary function. These streams are able to work flawlessly allowing people to perform visually guided actions with the slightest effort. Thus, in this pilot study we aim to investigate how the neural mechanism of the human visual system can be applied to help two people to collaborate in a seamless manner. In particular, we explore how the neural mechanisms of the human visual system could be used to allow two people to complete a collaborative task without the use of any verbal communication. As a result, this preliminary study revealed some intrinsic characteristics of the underlying strategy that makes any collaborative venture effective.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".