(www.interscience.wiley.com) DOI: 10.1002/acp.830 Estimating National Populations: Cross-Cultural Differences and Availability Effects
Bibliographic record
Abstract
Estimates of national population were studied in two experiments. In Experiment 1, Canadian and Chinese undergraduates rated their knowledge of 112 countries and then estimated the population of each. In Experiment 2, Canadians rated their knowledge of 52 countries and then provided population estimates for these primed countries and for a comparable set of 52 unprimed countries. In Experiment 1, participants from both nations produced estimates that resembled those obtained from Americans in prior studies (Brown and Siegler, 1992, 1993, 1996, 2001). However, there were several reliable cross-national differences in performance which appear to reflect cross-cultural differences in task-relevant naive domain knowledge. In addition, both experiments produced findings consistent with the claim that availability-based intuitions play an important role in this task. In Experiment 1, cross-national differences in rated knowledge predicted cross-national differences in estimated population; in Experiment 2, primed country names elicited larger population estimates than unprimed country names. We conclude by arguing for the general utility of this hybrid approach to real-world estimation. Copyright # 2002 John Wiley & Sons, Ltd. It has been estimated that the Khmer Rouge killed over 1 million Cambodians between 1974 and 1978. As terrible as this figure is, one needs to know the population of the
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.721 | 0.738 |
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".