Bibliographic record
Abstract
During the middle of the twentieth century, Frauke’s German-speaking family fled her birthplace of Hungary when she was a child, going first to Austria and then to Germany. They came to Canada when she was still a teenager, and Frauke has been living there ever since. She has had a full life, getting advanced career training, working in various office jobs and eventually reaching management level. Now nearing retirement age, she sometimes complains about not knowing the German word for ‘retired’ and finds herself inserting the English phrases ‘head office’ and ‘investment company’ into her German sentences when she talks about her life in Canada, but she still speaks the language of her childhood well, and she is proud of that. She met her husband in a German ethnic club, and she meets up with a group of friends from her old workplace at least once a month in a German café, where they always snack on German pastries together, drink coffee, and catch up on life in their shared native tongue. ‘We’re all German,’ she explains in an accent and with a grammar that only ever so slightly betrays where she has spent her entire adult life. ‘That Germanness is what keeps us together. I’m a Canadian citizen, but my heart is German.’ These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.252 | 0.093 |
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".