Bibliographic record
Abstract
I chose this topic for inquiry because I have personal experience with bullying, and I wanted to know what I can do to combat bullying when I am an elementary teacher. Through an online survey and personal interviews, research has been conducted to answer the question whether or not the increased availability of technology to students in, and out, of the classroom has an impact on bullying. My data collection, through the use of an online survey and personal interviews, has led me to believe that the increased availability of technology does have an impact on the number of cases and severity of bullying. The second question that I set out to answer is what can teachers do to recognize and prevent bullying in their classroom. In a twenty-first century classroom, students need to be educated on how to safely use the Internet. This could include administering a digital citizenship course to students. Teachers must also be aware of the warning signs of bullying in order to recognize when it is happening and stop it in its tracks. The best way that teachers can combat bullying is to educate themselves, and their students about the dangers of bullying. As a future teacher, this information is important for me because it will allow me to do more to combat bullying in my classroom.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".