Unveiling the Right Side: A Conversation with Pheidias and Pericles about the Elgin Marbles and Other Matters
Bibliographic record
Abstract
Just before a scientific conference in Porto Heli, Greece, in September of 2009, I visited the newly opened Acropolis Museum in Athens, which is very close to, and overlooks the old hill and the ruins of Parthenon. It was a busy day and the museum was packed with people. I really felt proud of being Greek, knowing that my ancestors created these beautiful antiquities. As I was glancing around, I saw two gentlemen who were strangely dressed, wearing ropes and sandals. At first, I thought these were two cuckoos, but as they came closer, I realized that they wanted to have a conversation with me. I introduced myself as a Greek biochemist living in Toronto and they returned the favor by introducing themselves as “Pheidias” and “Pericles.” My God, they were crying like kids! We sat in a corner of the museum overlooking the ancient field and the ruins of the Parthenon on the Acropolis and started chatting.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".