Connecting people with city cultural heritage through proximity-based digital storytelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.039 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it