Bibliographic record
Abstract
On Google Maps you can get navigator voices in English with either an American, British, Indian, or Canadian accent. I think they should offer a Texas navigator accent. We’ll call the navigator Jim Bob. Of course he couldn’t just have the accent. He’d need Texas expressions and colloqualisms too. Jim Bob would give you guidance something like this: You’re fixin’ to wanta take the ramp for Dallas up yonder ‘bout half a mile. You might oughta get in the left line right quick. Now, if you miss your exit, Jim Bob will say, Where you goin’? Missed your exit back there. It don’t matter. I’ll fix it. Grab the exit onto the feeder road and then take the Texas turn around. That’ll head you north and we’ll be back in business. Up the road a piece you’re gonna come to a four-way stop. You’ll see a Dairy Queen catty-cornered to you there. Take a left. You can ask questions of Jim Bob, like how far to Marfa? Tell you what, that’s not the question to ask. It’s a fur piece out there. What you wanta ask is how many HOURS to Marfa? From Brownsville, 10 hours. Unless you wanta take the scenic route, then I’d just say tomorrow. Any Starbucks ’round here Jim Bob? Nope. But, you got a great BBQ restaurant just a mile down that dirt road to your left. Down home and authentic BBQ. Plenty of ice cold Shiner Bock and Big Red too. Jim Bob, is this the right road to Dallas? Yep, best way. You’re already headed north on I-35. Just stay on it. Don’t give up. You’ll get there eventually – but you’ll likely be as frazzled as a kitten in a cactus patch when you arrive. In settings you can select surly Jim Bob. He begins most responses with “Do what now?” Jim Bob, what’s the traffic like in Austin? Do what now? What do you think it’s like? Same as always. A slow-moving train wreck. It’s got more bottle necks than Jack Daniels. Jim Bob, how far is it from Beaumont to El Paso? Do what now? I’d be ashamed to ask that question if was I you! How can you not know that? How long you been in Texas anyway? Also available in settings: Jim Bob, navigator … and life coach. Jim Bob, how fast can I get to Sixth Street? You know its 1:30 a.m.? Your mama wouldn’t want you goin’ over there this time of night. I’m not gettin’ between you and your mama and you shouldn’t either. Jim Bob, could you plot a course to Chicago? What do you want to go there for? Tell you what, nothing but cold up there this time of year. Nothing up there you can’t get here in Texas anyway, except for unbearable cold. Yup. I think we need a Texas accent navigator, to have Jim Bob along even outside of Texas, guiding you up Rodeo Drive in Hollywood, along Rockefeller Center in Manhattan or by the Washington Monument in Washington, D.C. Those warm Texas tones would be like comfort food far away from home. Hey, Google. You listenin’?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.547 | 0.385 |
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".