Connecting people with city cultural heritage through proximity-based digital storytelling
Bibliographic record
Abstract
This paper describes a research investigation on a project led by two libraries, Hamilton Public Library and McMaster University Library, in Hamilton, Canada, concerning the use of proximity-based technologies to share digital stories about a city’s culture. Proximity-based technology systems, such as iBeacons, allow users to receive information automatically when they are close to a physical spot. The project involved the setup of iBeacons that disseminated digital stories pertaining to Gore Park – a prominent historical park in the heart of downtown Hamilton. To test the viability of using iBeacon technologies to raise interest in a city and promote appreciation for a city’s cultural heritage, a pilot study was conducted. The study included one-on-one interviews and a short survey with 50 participants from the general public immediately after these participants used an iBeacon app to experience digital stories about Gore Park. Findings suggest iBeacons are viable tools to share city cultural heritage stories that yield improved perceptions of a city and greater appreciation for a city’s culture and history. Participants were appreciative of the digital stories and the iBeacon app. All participants mentioned that they learned something new about the city and that the app was very informative. Findings indicate that individual differences are important and can affect not only the acceptance and use of an iBeacon digital storytelling app, but also the extent to which the app can promote interest in a city and appreciation for a city’s cultural heritage.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".