Bibliographic record
Abstract
I don’t write unless I get invited to. It’s not that I’m so important or famous or in demand. It’s that I tend to have a comically bad time getting through blind peer review, to the extent that it takes me years sometimes to get an article accepted. In at least one instance, I had a conference paper proposal rejected by a reviewer acting on behalf of the same program committee that, simultaneously and on the basis of the same research, had invited me to deliver the keynote address. An editor at a flagship new media journal very strongly pushed me, in the first piece I had written as a new professor, to take out all the things that had made the article fun for me to write, things that made it more readable, vivid, and effective. It was a paper about rhetoric and metaphor that was forbidden from employing rhetoric and metaphor to make its argument. The fate of the following sentence was the subject of a surprisingly long email chain: “Language may shift our view of the world, but a popular consensus on vocabulary and metaphor does not necessarily alter the material operation of that world: tucking a flower into a gun barrel creates a powerful visual symbol, but does not preclude the florist being shot” (Morrison 74). These editors, and others since, operationalize the idea that scholarly writing has a specific voice in which its arguments must be expressed, which seems to be much flatter, less spiky or silly,1 literal rather than figurative, impersonal, distinguished from my own style of writing by its formality and earnestness.
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 | 0.000 | 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".