Bibliographic record
Abstract
In the modern digital era, online harassment, hate, and abuse have emerged as significant challenges affecting people across the globe. Immigrants, in particular, encounter unique cyber threats due to cultural differences, language barriers, limited social networks, and unfamiliarity with local laws and cyber regulations. This research focuses on examining the issue of online hate and harassment targeting immigrants within online communities. Through a comprehensive online survey involving 62 immigrants in Canada, we investigated their experiences in three key areas: Social Media Usage, Hate and Harassment Experiences, and Awareness. The findings indicate that immigrants have a high level of social media engagement (around 97%), but they also face a higher incidence of online abuse compared to global and US-only samples. Approximately 58% of the participants said they experienced online abuse, which included offensive name-calling (35%), stalking (6%), and sexual harassment (19%). Despite their active online presence, a significant portion of participants (72%) demonstrated a lack of familiarity with the specific Canadian laws pertaining to online harassment, and 10% had no knowledge of these laws at all. In this study we also examine recent platform solutions and navigates the challenges of balancing privacy, accountability, moderation, and free speech in protecting users in cyber spaces.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".