Investigating the Potential of Tabletop Natural User Interfaces Tools in Improving the Nunaliit Cybercartographic Atlas Framework
Bibliographic record
Abstract
This study presents the results of an on-going work, which offers a comprehensive qualitative analysis of natural hybrid interfaces (touch-based, gesture and tangible interfaces), as a human-computer interaction technique that has the potential to promote a methodological approach to the design of a new form of collaborative tabletop interface within the Nunaliit framework and its cybercartographic atlases.The study provides empirical evidence for the feasibility and value of incorporating collaborative interactive large displays in a mapping creation process.The results of this study are based on a usability study comprised of twenty participants and semi-structured interviews with ten professionals from various fields and experiences.The study confirms the potential benefits and applicability of employing this novel approach as an alternative to the more conventional user interfaces that are currently in use.The study offers several insights and design guidelines which will be indispensable when implementing this novel interface, particularly in the new collaborative Nunaliit framework, and offer a new opportunity for cartographic mapping contexts in general.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".