Indexing and Researching National Event Coverage in The Puget Sound Trail, 1963-1965
Bibliographic record
Abstract
The Puget Sound Trail, the student-run newspaper at the University of Puget Sound, has been published under various names and formats since 1895 and is one of the best sources on the history and culture of the University over the years. Many issues of The Trail have fortunately mostly been digitized and are currently housed in Sound Ideas, a digital archive run by the University. While this digitization has made it much more accessible and easy to use, I noticed some major problems that still block full utilization of this great resource, including a lack of an index that would allow finding specific subjects and a confusing search function. To begin remedying these problems, I indexed each issue in the academic years 1963-1964 and 1964-1965 as an example of how the rest of The Trail database can be streamlined to allow for greater use. I also did a small amount of research on how national events were covered in the The Trail during those same years as an example of how the new index can be useful for anyone who might want to do research or even casual browsing. The implementation of this index will allow greater access to and awareness of the value of The Trail and the Archives & Special Collections of the University of Puget Sound.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.025 | 0.070 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".