USING PASSPORTS AND PASSPORT APPLICATIONS FOR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 1931, my great-grandmother applied for her passport which was issued in London, England. She probably travelled up for the day from her home in Haslemere, Surrey, which is on a direct train line to the City. A month later, the passport was stamped for entry into Canada at Toronto. Four months later, an exit stamp appeared for Vancouver and only a few days later an entry permit was issued at San Francisco for the United States. One month later an exit stamp from the port of New York appeared and that was that. The passport expired and a new one was never issued, although it remained in her possession until her death, when it came to her youngest child. In 1973, I had the chance to see the passport and was able to match up the dated entries with my mother's story of her one and only contact with Grandmother Kingshott, who travelled across Canada and the States visiting her 5 immigrant children and their families. Having done her duty she never left England again. The passport, however, remained as a souvenir of the trip and allowed me to copy her rather grim passport photograph- one of only three I could ever find of
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it