Bibliographic record
Abstract
This is a hijacked column. It is normally known as Murray's Mumbles and always contains Musings from the President. Since Murray has been profiling each of the other EC members, it is only fair that the tables are turned this month-- so this is an interview with Murray. After High School in Victoria, British Columbia, which is on Vancouver Island, Murray worked on a ship in the engine room and they went back and forth to ALASKA! Now we can understand why he is not wildly enthusiastic about going on cruises in those waters. The Korean war was on and, being a loyal young man, he joined the Royal Canadian Air Force. He said he had hopes of going there and becoming a HERO! Alas, the war soon ended and Murray was sent to Germany as a Military Policeman. Following that tour, he was sent to Carleton University to study criminology and then graduated from the Royal Canadian Mounted Police (RCMP) Staff College. He was an officer with the Special Investigation Unit and served in Germany during the 'Cold War'. When asked what he did there, he said, ? I could tell you but then I'd have to kill you!!? :-) OLD JOKE!!! After 25 years, he retired and went to work as a security manager for a multi-
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.791 | 0.620 |
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".