Bibliographic record
Abstract
It is my pleasure to talk with you this morning about my son Hamed. He was a good boy who was bullied so much that he did not know what to do and so chose to take his own life on March 11, 2000. I cried very hard when I found out. In fact I screamed and called him to come to me and give me a big hug because I could not believe he was gone. I miss him so much. I lost Hamed because he was tortured and tormented by his schoolmates. I felt helpless, confused, sad, lost, and frustrated for a long time. I still do. My heart is broken forever but I decided to talk to you today hoping that perhaps by talking about Hamed and what happened to him it might help other mothers and other children who are suffering. I read to you a talk I gave to students and teachers at a Youth Forum called Diversity and Respect held Sunday, March 21, 2000. That day was called Hamed Nastoh's Anti-Bullying Day.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".