Bibliographic record
Abstract
first realized 1 had the language competency of a French four-year-old upon meeting a native Quebecois the final week of school.The problem was, this revelation came a week and a half before 1 was set to study in France for two months.1 was riding around the parking lot of Brookside Park on a friend's mini motorcycle, one similar to the type you see clowns wobble around on in slow paced parades.My friends and 1 had all taken a spin on it, some better than others, when a tall, dark haired man wearing a large camera and strap around his neck walked up to us."Hallo. 1 was wondzering if you could tell me what is zat?" he asked my friend Steve.A small red siren went off inside my head.Man Dieu! Zat was a Frenchman."Did you hear him?" 1 whispered to my friend Missy, my eyes wide and eyebrows arched."He's French. 1 know it.1 just know it."1 watched him ride around the pavement, his mouth open and laugh high.As he dismounted the vehicle made for parading Shriners and posed for a picture with it, 1 approached him, ready to make his night by speaking to him in the language of love.This man probably hadn't spoken French in weeks, even months.1 would be performing a service for the greater good of our global family."Are you from France?" 1 boldly asked him as he muttered his "Zank yous" to Steve and the rest of us."No, 1 come from Quebec. 1 am studzying Enzineering here for za zemester," he responded.Ah, not a true Frenchman, but a breed nonetheless. 1 could still use my skills and expertise on him."I wondered because 1 speak French," 1 said, my voice leaping an octave."Really?" he said.He went on in French.At least 1 thought it was French.This is what 1 heard in English: "I have not fdjsiflf soklestw in the United States.""What?" 1 said, my neck jutting forward like a clucking chicken.He repeated again, "I said, 1 have not afjklcxui person in the
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.444 | 0.213 |
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".