Bibliographic record
Abstract
As she rushed to turn on the computer, her heart pounded. Clicking on the web site, , her worse fears were confirmed. Shelia Frazier was mortified as she gazed at the message board. "Sheila is a fat cow", read the first message. "The slut of Baker High is Shelia Frazier," another message screamed at her. "Hey guys and gals for a good time call the freak of Baker High at 222-3030", read the next message. Tears streamed down her face. "How can I go to school tomorrow and face all those awful girls?" She remembered the words of Ashton, "You'll be sorry you didn't join our club. You think you are better than us. We'll show you!" As her cell phone began ringing, fear and dread wrapped around her like a cold wet wool blanket. Panic stricken, she wondered if she had a prank call. Sobbing softly, she felt alone. Worry, fear and dread engulfed her as she thought about the lies posted on the Internet web site. Not only could the kids in the high school see the nasty messages, the whole world now had access to her cell phone number! Shelia knew those girls were bullies at school, but she thought," This has gone too far. What can I do?"
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".