Supplemental Material for Egocentric Anchoring-and-Adjustment Underlies Social Inferences About Known Others Varying in Similarity and Familiarity
Bibliographic record
Abstract
Stimuli 50 Items on Preferences and Habits (Original Set Administered in Experiments 1 and 6)1. Spend an afternoon looking through childhood mementos 2. Go to the movies 3. Spend an hour in a coffee shop with friends 4. Spend half an hour texting with a friend 5. Do a crossword puzzle 6. Go out to a night club 7. Eat a piece of chocolate cake 8. Sing karaoke with some friends 9. Run on a treadmill at the gym for 30 minutes 10.Spend an hour reading a best-selling novel 11.Likes to have ice cubes in a glass of water 12. Spend an afternoon playing video games 13.Play a game of pool at a local bar 14.Throw a birthday party for a friend 15.Take a yoga class 16.Talk on the phone with a family member 17.Visit the dentist for a check-up 18.Like water more than juice 19.Spend half an hour browsing a friend's photos on social media 20.Make a late-night pizza run 21.Go over to a friend's to play a new video game 22. Re-arrange the furniture in your home 23.Go bowling with friends 24.Sample some cookies that a friend baked 25.Spend an hour organizing your closet 26.Spend the whole day in pajamas 27.Create a playlist with some of your favorite songs 28.Chew gum 29.Drink coffee 30.Watch a reality TV show 31.Like peanut-butter and banana sandwiches 32.Like to attend music concerts 33.Play charades with some friends at home 34.Like taking the train 35.Do laundry regularly 36.Generally have a positive outlook on life 37. Play a board game with some friends 38.Read a comic book 39.Recognize a movie star walking around town 40.Like Coke more than Pepsi 41.Watch a romantic comedy 42.Change the background image on your cellphone 43.Get your hair cut regularly 44.Help a neighbor move to a new place on the other side of town
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.622 | 0.081 |
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".