A statistical analysis of the use of a business game
Bibliographic record
Abstract
Fred NoNurray of Rumble Oil and Refining Co. of Houaton, Taxaa, haa dona aoma raaaarch into the effectiveness of their business game.Hie was a paychologlcal reaaaroh project dealing with personal reaponses of partlclpanta to questions suoh asi "Are you satiafled with tha reaulta of thla quarter for your company?" or "Did you feel you were the only one of your team who knew what the right .1 decision waa on the declalon?"No publiahed data was ever found oonoeming this project.Some colleges such as Tulane Uhlveraity and Cornell Uhiveralty are conducting some research into the area of business gamea at the preaent.Also at Clarkson College under the leadership of Dr. L* W. Iferron reaaarchera are trying to validate the 2 results of their gaming experience.At Carnegie Tech under the leadership of Dr. Kal Cohen researchers are trying to determine how objective gaming ahould be used and where it fits into the larger con< text of their over-all training and educational activities.Thay are planning to ccxnpare effects of a very complex business game with the effects of simpler games.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".