Bibliographic record
Abstract
Broadly defined, social stratification is an important part of many areas of study in sociology, but it also constitutes a distinct field on its own. Simply put, social stratification is the allocation of individuals and groups according to various social hierarchies of differing power, status, or prestige. Although divisions are often based on gender, religion, or race and ethnicity, the present entry focuses largely on socioeconomic inequalities, for the most part leaving other forms of social inequality to other entries. In this regard, social stratification is found in every society, even if it takes on slightly different forms. Uncovering what accounts for differences in social stratification—among societies and within particular societies over time—is a long-standing goal of the field. The classic works of early stratification sociologists—spurred by the work of Marx, Weber, and Durkheim—tended to be concerned with the question of “why” and “how” stratification arose in the first place. Although this debate continues to be an underlying motivation for much research on stratification, empirical research typically tackles questions for which evidence is more tangible. By the 1950s, stratification research was increasingly concerned with social mobility, though mostly within individual countries. By the 1980s, explaining cross-national differences in stratification became an important goal of the field. By now, stratification research is characterized by several debates. Although it has received somewhat less attention in the past decade or so, a classic debate centers on how socioeconomic position should be measured. Emphasis here has been on the applicability of measures of social class, status, and prestige. Although there are certainly important exceptions, differences in approach generally fall along territorial lines. European sociologists have tended to focus on relevance of occupation-based measures of social class, while North American sociologists have tended to rely on measures of socioeconomic status, which incorporate education as well as occupation. There have also been debates regarding the most effective ways to measure class and socioeconomic status. Yet other debates center on the importance of incorporating race and gender in studies of stratification. Finally, in recent decades emphasis has moved to the importance of education, both as a source of stratification on its own, and how it affects economic inequalities.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".