Appendices to 'Respectful and Responsible Relationships: There’s No App for that (The Report of the Nova Scotia Task Force on Bullying and Cyberbullying)'
Bibliographic record
Abstract
Chairing this Task Force and producing this report has been both the most engaging and exhausting project that I have ever undertaken. Since my appointment in late May 2011, I have lived and breathed in the world of bullying and cyberbullying. I am sure my fellow Task Force members and members of the Working Group did the same. Born in the wake of tragic teen suicides it was easy for the members of the Task Force to be motivated. Indeed, few ventures have stirred my passions as much as this exercise has. Bullying is a major social issue throughout the world and is one of the symptoms of a deeper problem in our society: the deterioration of respectful and responsible human relations. The magnitude of the problem is daunting and there are no simple solutions on the horizon. There are, however, some effective strategies. The advance of technology and the prevalence of social media are profoundly changing how we communicate, and in so doing, they are also changing who we are. While the mandate of the Task Force is to focus on youth, the underlying problems are not unique to them. I was under no illusion that we would solve the problems of bullying and cyberbullying but I do think that our recommendations, if implemented, will make lives better for many young Nova Scotians. The pain caused by bullying is widespread and the consequences are drastic. In the age of the Internet, cyberbullying knows no boundaries and it permeates all aspects of the victims’ lives. It is also corrosive for the bullies and the bystanders as well, and one role sometimes morphs into another. It has been a privilege to lead this important task force and I trust we have made a difference by charting a course to a more humane, caring world: one in which technology is the enabler rather than the master, and where social media is used to celebrate our differences rather than to attack those who are different. As adults we can provide better role models of respectful and responsible relationships and set high standards for the younger generation to follow. Bullying and cyberbullying are community problems and we all must play a part in finding the solutions. I hope this report will be a roadmap to guide us on this important journey.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".