Bibliographic record
Abstract
This past week, I sat in a small, windowless room with the express purpose of talking to a blank screen. I had been asked to participate in a Skype conference, and - for the first time - unaccompanied by an adult (otherwise known as a technician). Yes… Arne did set up the machine, align the entire program and show me how to work it with detailed instructions. But, it was my responsibility alone to push the “call” button and contact the meeting coordinator. So, after Arne left me to my own devices, I sat there with a bead of sweat on my upper lip as my finger twitched to push that button. My mouse hovered for a while, and then… I hooked up. It was almost instantaneously answered and the coordinator’s familiar face floated on the screen. Courting hubris, I congratulated myself on this operation. We actually chatted for a few minutes when she received another call. All of a sudden things started to become choppy and disoriented on the miniature world in front of me. Then the coordinator said the last words before the image went inky black: “hmmm… maybe if I press this…”
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".