Connecting people with their communities through proximity‐based digital storytelling
Bibliographic record
Abstract
ABSTRACT This visual presentation describes the results of a library‐led initiative involving the use of iBeacon proximity‐based technologies to connect people with their communities. The two libraries involved in this initiative developed and tested an app that pushed community‐based digital stories to users' smartphones and tablets when users were physically near a historic or cultural object (e.g., statue, landmark) about which the digital story was based. Using proximity‐based technologies to deliver stories about cultural and historic objects of interest to a community was thought to be a novel and engaging way to rally emotional connection and pride in one's own community. To test this hypothesis, as well as the viability of using iBeacon technology as a storytelling delivery mechanism, a pilot study was conducted with 50 participants from the general public at the two libraries involved in the initiative. Participants used the app to browse and read stories pertaining to a prominent park in the city. Posters with pictures of historic or cultural objects within the park were installed throughout each library. Each poster was enabled with iBeacon technology so that digital stories about the historic or cultural object displayed on the poster were automatically pushed to participants' smart devices as participants became near. To collect data on users' experiences and satisfaction with the app and the digital stories, on‐the‐spot interviews were conducted with participants after they finished visiting the posters and reading the digital stories. Interview data were analyzed using content data analysis techniques. A Likert‐scaled questionnaire was also administered; this data was analyzed using factor analysis and hierarchical regression. Results suggest that proximity‐based digital storytelling is a viable approach to connect people with their communities, especially if the stories provide people with relevant, timely, location‐based community information that stirs a positive emotional response.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".