The Modern Tall Tales Texans Tell Kids
Bibliographic record
Abstract
We Texans have long had a reputation for tall tales, for stretching the truth in entertaining ways. I wondered to what extent this cultural DNA has survived. So I asked this question of Texans on Facebook: What is the most outrageous white lie you ever told your kids? I got several hundred responses and chose these as the best among them. Cynthia told her kids: “Oh. The smoke detector is a Santa camcorder. How do ya think Santa knows if you are naughty or nice?” Jim said that his uncle taught them that windmills were cow fans. Kept all those cows cool in the hot months. Many wrote that they told their kids, “The ice cream truck turned on the music to signal that it was out of ice cream.” This bum steer was so popular as a submission that it no doubt rates as an urban legend for kids. Tammy said that as she passed the cotton fields on the way to Port Arthur she would say to her northern-raised grandson: “See, we grow our snow here.” And from Rose we have this: “To get my boys to let me trim their nails we would plant them in the window box and watch them grow.” Rose actually planted one bean for each. Very clever Rose. Tammy P. said, “I had my kids convinced that I could see through walls because all moms had superpowers.” Rhonda had a great one that she told her children. “Sorry kids, you can only go to Chuck E. Cheese if you’ve been invited to a birthday party.” Evidently a company rule. David had his youngest daughter convinced he could see through walls. He told her to run to any part of the house and he’d tell her where she was. Dave just had to listen to her footsteps and never missed. His daughter was blown away by his omnipotence. Leah told her kids she was a retired ninja. She had an impressive large necklace that looked like an award and so that was her secret ninja badge. Unfortunately her ninja suit was always at the cleaners. Kris would tell his kids Twilight Zone stories as though they happened to him. It was part of his autobiography. I love this from Samantha: “When you go through the drive-thru they give you car fries and house fries.” So once the kids had had a few fries, she’d say, “Sorry, that’s all the car fries they gave us. Have to wait now until we get home.” Glynda said her kid wanted to ride the elephant at the circus and she said, “You need an elephant riding license for that. Unfortunately, we don’t have one.” And we have this about a fish tank where all the fish died. Kristi recalls, “Well, we were cleaning the tank and its contents, and preparing the water for new fish while we waited for pay day so we could buy more. The kids were disappointed when they came home from school and there were no fish. So I convinced them that we had bought ‘crystal’ fish that are crystal clear. I told them if you watch real close you’ll see the reflection of the lights on their scales occasionally as they swim by. Entertained them for days.” Karen M. has the tallest tale I think, if not the most devious. She said, “My youngest refused to eat meat (or any protein) as a child. From 3 to about 12, my kids believed I would take them to the doctor for a ‘meat shot’ if their protein wasn’t eaten. I showed them the meat shot injector, my turkey baster.” So, like I said, I’m glad to see we Texans have not lost our talent for tall tales. Edward “Tex” O’Reilly, creator of Pecos Bill, would be proud of us.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.016 |
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".