Bibliographic record
Abstract
In the last thirty years elites have been forgotten in social sciences but remembered as capitalism has come to reward them in ever more stark ways. These essays from the ESRC Centre for Research on Socio Cultural Change (CRESC) bring together an interdisciplinary team including sociologists, anthropologists, political scientists and management researchers all arguing for and demonstrating the need to resume elite studies. The issues which this collection explores include: - How to (re) conceptualise elites in present day capitalism? - What methods do we need to study elites? - How significant are elites as social and political agents? - How has financialisation shaped elite formation? - How are intermediaries important? - What about cultural elites? In pursuing these common issues, the book includes empirical studies of the UK, Canada, Greece, France, as well as various international institutions. A wide range of methods, from survey analysis through social network analysis, ethnographic research and documentary analysis is used to make this the most wide ranging and ambitious engagement with elite studies to have been published for many years.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".