Bibliographic record
Abstract
AMANDA REA Helping Alberta is not my sister.Everyone seems to know this but her.She's Hispanic and I'm not.She is thin and her nose is like a hook, whereas my features are all rounded.We have never even been mistaken for sisters.It's like this: Alberta is my mother's first husband's ex-wife's stepdaughter.She claims it's the same thing as sisters but I doubt it.I don't remember her stepfather and she never met my mother, and for me, knowing the same parents is a requirement.But here we are.She is grafted onto me where a sister might have been.Six months ago, Alberta left her husband at an intersection in Albuquerque.It was a cold Tuesday, they were on their way to Wal Mart.They had eaten toaster pastries for breakfast and Clark still had some frosting in his beard.That's how Alberta describes it.He was driving and fiddling with the radio with his hairy hands.Light went red, traffic stopped.It was an old car, so the brakes squealed.Alberta looked down to find her hand already on the door handle.She hadn't even planned it.She closed her eyes to memorize the way things felt in the car: the muffler rattling, heat blowing on her feet, something scrambling in her stomach.She got out.Clark leaned over to watch her.His mouth hung open in a way that she had always disliked."Alberta, what the hell?"She looked up the street, north toward Wal-Mart, then south the way they had come.It was a gray morning in both directions."What the hell are you doing?" Clark threw up his hands."We're almost there."The light turned green and the driver behind him honked, so Clark had to drive forward.He shouted her name and tried to pull over, but there was no shoulder.There was more honking, and Clark accelerated.Alberta took a deep breath of exhaust and waved to the rear window, where her son watched with a pale, sleepy face.She borrowed a car and drove north, to Colorado, where she spot ted me in line at the supermarket.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".