{
  "name": "voiced lives against real lives: does a voice know which women go on to have a child?",
  "registered": "2026-10-04",
  "after": "results-voiced-fertility.json: voices overstated the level about threefold, so a voice may move a life only around the population's hazard. This asks whether that movement carries real information about which lives act.",
  "data": {
    "source": "General Social Survey panels 2006-2010, 2008-2012, 2010-2014 (NORC, public, Release 6), waves 1 and 2 only (two years apart)",
    "who": "women (sex_1 = 2) aged 18-43 at wave 1 with a valid number of children (childs 0-8) at waves 1 and 2",
    "outcome": "a child between the waves: childs_2 > childs_1",
    "facts": "at wave 1 only: age, marital status, number of children, highest degree, work status, and the survey year"
  },
  "split": "respondents into two halves by the seed: train and test. Every fitted number is fitted on train only; every score is on test only.",
  "voice": {
    "model": "qwen3.5-4b through Living Worlds' own inference [infrastructure redacted], thinking off, temperature 0, at most 16 tokens",
    "prompt": "system: You are a woman living in the United States. The facts of your life are below; answer as yourself. user: the facts in plain words, then: It is <survey year>. How likely is it that you will have a baby in the next two years? Reply with only a number between 0 and 1.",
    "unparseable": "did-not-run: reported, and left out of every method's score alike"
  },
  "methods": {
    "age": "logistic regression on age and age squared (train)",
    "facts": "logistic regression on age, age squared, married or not, never married or not, number of children (capped at 4), degree (0-4), working full or part time or not (train)",
    "voice": "the voice's own number as the probability",
    "anchored": "logit p = logit(age) + b * (logit(voice) - mean logit(voice) of train lives within 3 years of age); b fitted on train",
    "facts+voice": "the facts regression with logit(voice) added (train)"
  },
  "outcomes": {
    "primary": "on test: area under the ROC curve (ranking) and log loss (calibration), per method, with 95% intervals from 2,000 bootstrap resamples of test lives",
    "decision": "(1) the voice carries information if AUC(voice) - AUC(age) has a 95% interval above 0. (2) The voice adds to a fitted model if AUC(facts+voice) - AUC(facts) has a 95% interval above 0. (3) Anchoring fixes the level if log loss(anchored) <= log loss(age). Each is reported whatever happens."
  },
  "seed": 20261004
}