What are Phases of Cyberbullying Victim’s Feelings?
Bibliographic record
Abstract
In 2003, one of Canada’s neighbours stood up to talk about cyberbullying for the first time. Bill Belsey defined it as follows: “Cyberbullying is the use of information and communication technologies to deliberately, repeatedly and aggressively engage in behaviour towards individuals or a group with the intent to cause harm to others [1].Cyberbullying cannot be compared to harassment in the real world, as discussed in one of my scientific publications [2]. The method, the impacts and the propagations are different than in the real world. In fact, the impact on the victim is also different. She does not feel the same reproaches, criticisms, insults, as in the virtual world and does not experience them in the same way.In fact, the emotional cycle from the moment of receiving the insult to reparation or resignation is different. This is what I have found when working on several cases of cyberbullying victims and their predators. I name this theory: “Phases of Cyberbullying Victim’s Feelings”In fact, the emotional cycle from the moment of receiving the insult to reparation or resignation is different. This is what I have found when working on several cases of cyberbullying victims and their predators. I name this theory: “Phases of Cyberbullying Victim’s Feelings”It is the fifth in my family of theories on Behavioral Differences between the real and the virtual [3]. “Avatarization”, “Transversal Zone”, “Virtual Intelligence” and “Modus Operandi in the virtual” as well as my books on net-profiling [4].Understanding these emotional phases of the cyberbullying victim allows to better apprehend the said victim and prevent him from committing suicide, but also to prevent the cybercriminal. The victim will also feel better considered.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".