Bibliographic record
Abstract
Th is project has been a joy to complete: historical work is usually a solo endeavour, but it has been so much more fun to do it alongside the brilliant, thoughtful, and hardworking Bethany Philpott and Sara Wilmshurst.I greatly enjoyed our many trips to Ottawa, gossiping about the Health League along the way.And I am grateful to both of them for their ongoing passion for the project, even while they both moved on to other, highly worthy activities.I also want to make special mention of Shawn Goodman and Caitlin Fendley, whose work was vital to Chapters 1 and 2 , respectively.My own history with the Health League is a long one.As a graduate student, I came across Health magazine in the gorgeous stacks of Gerstein Library.Sitting on the translucent fl oor and shifting through the magazine's later issues, I was shocked and amused by its pervasive moral tone.Later, when I turned my attention to the history of water fl uoridation, I became aware of the important role that the Health League had played in that campaign.Curious, I spent a few Saturdays in Gerstein going through issues of the magazine from the 1950s and became convinced that the Health League was a worthy story in and of itself.Soon afterwards, I was teaching a class on the history of disability, which attracted a number of extremely bright, engaged students.When the class ended, Shawn Goodman asked to do a senior thesis with me.I suggested
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.400 | 0.267 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".