From the Sandbox to the Inbox: Comparing the Acts, Impacts, and Solutions of Bullying in K-12, Higher Education, and the Workplace
Bibliographic record
Abstract
As research advances in the areas of bullying, cyberbullying, and harassment in various sectors, it is a useful endeavour to consider the connections between research studies conducted in what may appear to be parallel spheres. In this paper, we examine the similarities and differences between research on bullying, harassment, and especially cyberbullying in the K-12, higher education, and general workplace sectors. First, we review the research literature on the nature and extent of these issues, taking into account variations in conceptual definitions, types of experiences, distinctions between different socio-demographic groups, underreporting, and prevalence rates. Next, we consider the range of impacts reported in the different areas. Finally, we examine the solutions proposed within each of these research literatures. Despite some contextual differences between the K-12, higher education, and workplace sectors, there are many commonalities among them in terms of the acts, impacts, and solutions, thus suggesting the need for a more concerted approach to these problems and a cross-pollination of ideas between the sectors for solutions.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".