Bibliographic record
Abstract
In this interview, Alberta Kearney is the guest. At the start of her interview, she talks about her needing to move away with her father, stepmother, and brother so she would be able to finish school. During her stepmother’s pregnancy, who was only two years older than her, she stayed and helped until her siblings were born. Though her father and stepmother later left each other, Kearney always had a bond with her siblings. Later on in Texas, she was able to get a job working for a prohibition officer by taking care of her young son. After leaving a note when the boy was not home that she would be going to beauty school and would return, she was given until that Thursday to work at that home. After securing a job elsewhere after being unjustly fired and ignored by the woman, the latter wanted her to continue working. She left after telling the woman she had a job elsewhere and comforted the sad boy. Kearney studied in Paris, passed her state board exam, and later went to work in California with her new beauty license. While being invited to the People's Independent Church in Los Angeles and joining to sing in the choir, a man took a disturbing obsession with Kearney. After he tried to assault her and got married to her, Kearney left him at around five months pregnant as she had no love for him. The interview ends with Kearney looking at some notes she had written down.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.250 | 0.042 |
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".