Examining the Impact of Increasing Location-Based Information Fidelity on Command Center Decision-Making
Bibliographic record
Abstract
The deployment of high-fidelity information systems in command and control environments is common, however it is not yet well understood what impacts these systems have on decision-making processes, or whether the implementation of these systems is always a positive change. Research in military domains has suggested that these types of systems can create substantial increases in micromanagement, but these changes have not been empirically investigated. In this thesis, the effect of high-fidelity information on command environments is experimentally evaluated. \n \nA baseline set of data is collected within a real-world command center that uses only low-fidelity information. Then, a laboratory-based controlled technology experiment is used to gather information about how the command processes change as information fidelity is increased. Finally, the same system is implemented within the functioning command center and a preliminary comparison is carried out against the original baseline data. The experimental study suggests that an increase in micromanagement may occur with an increase in information fidelity, while increases in situation awareness and performance improvements during times of both extremely low and high workload are seen. The preliminary ecological validation study shows support for these effects.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".