Bibliographic record
Abstract
DOMINIC MCGILL > It was clear from the beginning that what Cem and I wanted to do was produce a historical narrative of the past ten years. We chose a panorama, for its ability to eliminate the horizon of the audience, and drew up a rough diagram of our major areas of interest. It was only after our first full-scale test, in which we hung a large sheet of paper on a circular track from the ceiling and made our first marks, that we fully grasped the double nature of what we had embarked on. To see the drawing, we had to light the center; doing so caused the drawing to bleed through to the piece’s dimly lit outside, and it was the unintended consequences of this that structured the next two years’ work. It was a reminder that words and Murat Cem Menguc is an assistant professor of history at Seton Hall University. He received a PhD in Ottoman history from the University of Cambridge and holds an MA in Islamic studies from the Institute of Islamic Studies of McGill University. Menguc researches Ottoman and Turkish historiography and is preparing his first book, “Making of the Ottomans: Identity and Sovereignty in Early Ottoman Histories.” He is a regular contributor to the Informed Comment: Global Affairs (icga.blogspot.com) and has written about Turkish history and identity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.108 | 0.034 |
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".