Bibliographic record
Abstract
I would like to conclude with another true and fictional story: “This conclusion is not going to work,” I told myself years ago regarding a paper I was about to submit. I said that not because I was a self-deprecating graduate student (although I was), because I felt the conclusion was also not going to not work. At the time, I wanted to perform a Derridian a-conclusion of indeterminacy. It would work the more it didn’t work. “Things are paradoxical,” I announced, “including that statement. And everything in this conclusion is a lie.” I toyed with various alternative formats. What if I wrote the conclusion straightforwardly, and then had a single endnote that ran for pages explaining why what I wrote didn’t work so the reader could have the narrative of progress and the questioning of that narrative. But everyone hates endnotes (never mind a 10-page one), and besides every sentence would have to end with the endnote to indicate the two narratives should be seamlessly read together, so I gave up on that idea. What about two columns of text: one side containing the normative illuminating narrative, the other the subversive, occluding one, similar to what Derrida did in 1974 with Glas (Derrida, 1986)? Or perhaps I could write the second half of the paper backwards and upside down so readers could symbolically and literally invert themselves/the book and see a contradictory narrative. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.178 | 0.092 |
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".