Bibliographic record
Abstract
<JATS1:p>There are groups in society that experience profound social problems. Others betray a growing social malaise. Massive academic underachievement, family dysfunction, substance misuse, violence, and delinquent behavior are some of the major crises afflicting groups in the United States and Canada, including Aboriginal people, African Americans, and certain Hispanic groups.^LTaylor adds to this list the escalating number of so-called street kids roaming inner-city streets. To a lesser but no less frightening extent, he includes what has traditionally symbolized society's most privileged group-young white men. He asserts that while these are not the only groups who stand out as noticeably disadvantaged, they are among the most visible and, due to his research and activities, allow him to test his arguments and offer his proposals for change.</JATS1:p> <JATS1:p>Drawing upon his research experience in Canada, the United States, South Africa, and Indonesia, Taylor examines the impact of assimilation and the policies of cultural diversity and multiculturalism on these groups. He offers surprising insights into the causes of group malaise and individual failure, and his conclusions are bound to be of significant interest to scholars, students, and researchers involved with intergroup dynamics and cultural diversity.</JATS1:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".