Bibliographic record
Abstract
I’m sitting in the car. Staring straight ahead at some stranger’s house. What the fuck is taking so long? How long does it take to exchange money for drugs? I’d honk, but the last time I did that Tyson flipped. I scan through the channels on the radio. Nothing on. I flick it to the Christian station. Sometimes AM is good for a laugh. It’s one of those shows from down south that gets rebroadcast up here. A guy who sounds like a cross between Boss-Hog and Jimmy Swaggart is describing hell, the gnashing of teeth, he says, along with a host of other evils, is awaiting fornicators and idolaters in the next life. Bullshit. I turn it off. It’s hot. But I’ve got to stay in the car. A cop drives by and I slink down in my seat. Fuck. I hate being seen here. This place is sketchy. Big fence, mad dogs, a ton of broken down cars, and the house has moldy cardboard covering most of the windows. A guy got knifed right here last week. I nervously tap out a rhythm on the dashboard. Then I see him. Coming out of the house. I can tell just by his posture that he scored. He slides into the car. Fuck he smells bad. Like I don’t know... rotten ass? God! I roll down the window and start to drive. “Whatcha get?” “Couple of those quick-release tens” “How many?” “Twelve” “That’s it? Twelve total? Six each?” “Yeah, you dick, it’s all he had. You don’t have to do ‘em y’know” “Sure. Yeah right. Let’s just go to my place and get fixed. Do you have some change for me?” I ask hopefully. “I owed him forty. He wouldn’t have sold to me if I hadn’t paid him what I owed. I get a cheque next week; I’ll get you back. Or you can wait till I get my script.” “Whatever. You’re an asshole” “Fuck off. Get your own hook up then” A Moment of Clarity Caleb Huntington
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".