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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".