Bibliographic record
Abstract
MurderI walk this neon block every Friday night around 1:31.And then again at 1:37, 1:43, 1:49, and 1:55.You always show up.Our planned encounters would not even be possible if it weren't for your stagnant nocturnal life, but I know I'm here in spite of it.I am making my fifth pass when I see your carnage-hungry face round the corner.You are wearing your Hooters shirt, and from the way your steps go, I can tell you are coming from a much darker place.I carefully note each stride, each stumble.I ignore the way your eyes chase women, and I prepare for our synergy.I have to be strategic about this.When our motions come to a point of potential meeting, I make my usual abruptly-cross-the-sidewalk-to-enter-thenearest-store move.Different things happen when I make this move.When you're sober, we brush past each other, I allow my body to graze your tan jacket, we politely smile, we succumb to society's polite faade.But when you're like you are tonight, we collide.I make sure of this.I do not have time to select the store into which I will turn, I have read your approaching expression for too long.At the last second I remember to count my steps: one, two, three, crack-in-the-cement, four, five, veer-to-the-left, six, false-hesitation, seven, crash.April 17.But our bodies, they tangle in just the right way.Our energies first meet at the shoulder -my right, your left.From there, the combination trickles down to our elbows, and I flirt with it, pulling back slightly before going back for more.I am waiting for the moment when your left hand will react, reach out, touch me, my hip, the small of my back.I remain in my state of faux fluster, and my surprised smile lingers, as I wait for your next move."Stupid bitch."And you murder me again.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.604 | 0.006 |
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; both teacher heads agree on what is shown here.
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".