Projections and Perceptions-Editoria Comments. Places
Bibliographic record
Abstract
I grew up in Newark, New Jersey. I loved its streets and its people. I loved the public schools, the No. 13 trolley that carried passengers down Clinton Avenue to the downtown area, to Bamberger's, Hahnes' and Kresge's, to the Newark Museum and especially the public library. For eighteen or more years I had never been outside of New Jersey or New York, but in the library I found the world. And I could float in my imagination to the Indian sub-continent, to China and Japan, to the great European capitals, to Oceania and beyond. I found a great fascination in maps and the questions they inspired. Where is Athens? How long has it been a civilized community? Why did it become the prime urban center of Greece? And what has happened to it through time? Katmandu, New Orleans, Dacca, the Great Basin, Jerusalem and Tel Aviv, Quebec, the Taj Mahal and the Red Fort, the Valley of the Sun, Thessalonika, Cairo . . . and so many others. Places. Memories.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".