Bibliographic record
Abstract
This has been my second summer as the IOT Tour Coordinator, last time it was in 2002. I took a summer off from this, and there were lots of changes. The building is bigger, the staff has changed a little bit, there is even a new DG. Some things are the same. People here are easy to get along with, there is a new batch of work term students that are fun to eat lunch with, and the weather is garbage. But you're in for a good time with a good bunch of people and a supervisor that's really easy to get along with and knows what he's talking about, so read my report for what its worth, and enjoy. Challenges: I have different challenges for the job this time around. There is no advertising budget for the tour coordinator this year, due to the federal sponsorship scandal. I have to find some creative ways to get the word out without any money! I have also decided to make it a priority to try to get more Francophone visitors into the Institute. I don't get to practice my French as much as I would like. I also had to ask myself when I saw the numbers of French tours, did I really reach out to French speaking people? Almost all the information was in English. And then some things are not as big a challenge as they were the first time. Despite the 2-year gap between even entering this building, I remembered a lot of the details of the tour. Once the information gets in there, future coordinators will be glad to know that it just sticks.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.424 | 0.299 |
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".