Bibliographic record
Abstract
INTRODUCTION Many countries around the world are becoming more diverse. There are a few countries, such as the United States, Canada, and, more recently, Australia, that have encouraged the emigration of diverse populations and pride themselves for their diverse citizenry. Imagine yourself as part of a committee of government officials who are trying to decide whether diversity of emigration should be encouraged or discouraged. To ensure that both sides get a complete and fair hearing, you have divided your committee into two groups to present the best case possible for each side of the issue. You assign Group A the position that diversity is a resource that has many beneficial influences. You assign Group B the position that diversity is a problem that has many harmful influences. The overall goal is for the entire committee to write a report giving their best reasoned judgment about what the emigration policy should be regarding diversity. Ideally, all members will agree. The steps of constructive controversy are then followed to ensure that the resulting interaction will enhance creative problem solving. “We should encourage the emigration of diverse populations to our country,” stated a member of Group A. “Not only will it increase our productivity as a country, but it will decrease the stereotyping and prejudice in our country and result in positive relationships. Opposites attract, you know.” “Nonsense,” said a member of Group B. “We have to stop any emigration of diverse populations to our country. Not only will it decrease our productivity by causing so many interpersonal problems on the job, it will increase anxiety and tension and strain in interacting with store clerks, colleagues, and neighbors. Forced friendliness actually is not easy. That strain cannot be good for people. Since people tend to like people they think are similar to themselves, there is going to be a lot of negativity and dislike among citizens. Stereotyping and prejudice is bound to get worse.”
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".