Bibliographic record
Abstract
know, and part of why I was so angry was because I knew there was still this boy to come.I didn't want four children; I wasn't even sure I wanted three kids, let alone four."But then something happened, and I really started to love Brooke; in fact, of all of them, I think Brooke is the one I really loved even before she was born...I don't know why; I think a lot of it is just to do with Brooke, actually, with who she is… and I really loved her.Then I said to God, 'Ok, I know what you want me to do, but I'm sorry, forget the Celestial Kingdom, I give up on it.I'm just not going to do it, OK, God?' Then I put it off for a number of years.By the time Eric was born, Brooke was five and Heather was ten… I wanted to keep my nice, peaceful life… "But then one night, I woke up in the middle of the night, and I knew there was someone in the room, standing next to the bed.And not being a very spiritual person, I didn't talk to this angelic presence or anything.ii I just stuck my head under the pillow….like, 'Leave me alone!'But I knew who it was; it was Eric.And I knew that this was one last plea for me to do what was right.And so, then I was, 'Alright.'And I got pregnant.But all the way through my pregnancy, I was really not reconciled to it at all; I was really fighting it.The night before Eric was born, you know, I was out here [makes gestures of very pregnant belly] and I turned to [Dave] and said [crying] 'I just don't want to do this!' "And it was interesting, because of the way I felt, I sought a lot of priesthood blessings during my pregnancy, and … they were all very accepting and very comforting.Not one of them was like, 'Get a grip!'They all kept telling me, 'Don't worry; when this baby is born, you will love him and everything will be just fine.' "And when Eric was born, the moment I saw him, you know, I just loved him.He was everything we had been promised.He's such a wonderful little boy; so kind and loving and
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".