Place, Path, and Community: Evaluating Strategies for Mobile Application Creation by Memory Institutions
Bibliographic record
Abstract
Memory institutions want mobile apps to be able to connect items in their collections or specific historical events to specific geographic locations. They also want mobile apps to be able to mediate tours extending across a series of related locations, and to create an experience of community for users. Using a range of research from tourism studies, game studies, and mobile interface theory, this paper assesses these goals and concludes that mobile applications are extremely well suited to connect user, place, and historical event or object. Les institutions mémorielles ont besoin d’applications mobiles capables de connecter entre eux des articles de leurs collections ou des événements historiques particuliers à des zones géographiques spécifiques. Ils ont aussi besoin d’applications mobiles capables de servir de médiateurs de visites s’étendant à une série d'emplacements connexes, et de générer l’expérience d’une communauté pour les utilisateurs. En utilisant un éventail de recherches puisées parmi des études sur le tourisme, des études sur le jeu, et la théorie de l'interface mobile, cet article évalue ces objectifs et conclut que les applications mobiles sont extrêmement bien adaptées pour connecter l'utilisateur, le lieu et l’événement ou un objet historique.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".