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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".