Focus Groups as a Means of Qualitatively Assessing the U-Boat Narrative
Bibliographic record
Abstract
This article presents the results of using focus groups to evaluate the U-Boat Narrative (UBN), a data exploration system focusing on the submarine conflict of 1939-1945. UBN was developed in response to the limitations inherent in existing methods used to convey information about the conflict, which generally fail to adequately convey the spatiotemporal aspect of the U-boat war's chronology. In response to these limitations, the computer, with its animation and interactive capabilities, was suggested as a possible solution. A prototype of UBN was developed that contained two components: narratives and data exploration modules. The narratives provided a background on the U-boat war through static maps, text, and pictures, while the data exploration modules allowed users to see an animation of Allied ships sunk and damaged, select from various attributes, and view several statistical and graphical representations. Three focus groups (made up of novices, historians, and cartographers, respectively) assessed UBN'S overall "look and feel," interface design, and usefulness as a data exploration tool. The narratives were appealing to all groups, while novices and cartographers especially enjoyed the pictures and wanted to see more of them throughout the program. The animation and attribute modules were intriguing to all groups, but the historians were particularly keen on the modules' ability to represent patterns of ships sunk by U-boats. The novices and historians concluded that, compared to the rest of UBN, the graphical summaries module was not enticing. While the cartographers agreed, they suggested alternative strategies that would improve the appeal of this module. In summary, the focus groups provided comments on the prototype's existing design and offered suggestions on ways to improve UBN for future versions.
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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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".