Exploring Digital Inclusion: Addressing Homelessness Through Equitable Design on TikTok
Bibliographic record
Abstract
This study examines the digital experiences of individuals experiencing homelessness on TikTok, focusing on their usage patterns, challenges, and opportunities for social connection. Through a review of literature and analysis of TikTok content, the study examines how individuals experiencing homelessness use social media, the challenges they encounter, and the potential benefits and risks associated with online engagement. Despite challenges such as network access, device quality, and privacy concerns, homeless individuals navigate digital spaces to share personal stories, seek support, and participate in online communities. The study identifies themes related to digital divide perceptions, survival infrastructuring, social capital building, and health information seeking behaviours among homeless populations on TikTok. Based on these insights, the study proposes platform-level and user-level recommendations to improve the digital experiences of homeless individuals on TikTok, focusing on bandwidth-sensitive design, enhanced privacy controls, and security toolkits. These recommendations aim to promote digital inclusion and support for vulnerable populations in the digital age, contributing to ongoing discussions about equity and social support online.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| 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".