Bibliographic record
Abstract
IllThe moment Miriam picked up that tambourine and began to dance, that's when I crossed the waters.For the others, the most important moment was the parting of the sea and the miracle of dry land beneath our feet and arriving safely on the other side, free of the slavery that had held us, and free of the terrifying pursuit of the chariots of the oppressor.But for me it really happened when Miriam danced.She picked up her tambourine and her song split the silence as Moses' rod had split the sea, and her voice rose and fell like waves.And she began to dance.'Round and 'round she danced, singing praise and thanks to God.And one by one the others joined her and it seemed as though it were a sacred circle and we were all being gathered into it.But I held back ... afraid.Maybe it was because all I had known since I passed out of the innocence of childhood was slavery.I was born a Hebrew in Egypt, and brought up there.My mother named me Dinah after an ancestress of our people who was brave and free, but all I really knew now was slavery.But you know, when I was a very little girl, I loved to dance.I used to dance while waiting for my breakfast, then dance as I helped clean up and watched my parents go out to work so hard making bricks for Pharaoh.I'd dance in the fields during the day to the tune of a bird's song or to a melody that was inside me but seemed to come from somewhere else.I'd even slip out of bed at night, sometimes, whirling in my nightdress and delighting in the way the lamplight cast the shadow of my swaying and twirling body on the wall of the room.Have you ever seen lambs leap in the field, suddenly, without warning?Or puppies chase their tails?Or kittens spontaneously jump
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.444 | 0.352 |
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".