<scp>Euroclash</scp>: <scp>Towards</scp><scp>A</scp><scp>Sociology</scp><scp>of</scp><scp>The</scp><scp>European</scp><scp>Union</scp> - Neil Fligstein, Euroclash: The EU, European Identity, and the Future of Europe (Oxford, Oxford University Press2008).
Bibliographic record
Abstract
North american sogiologists have shown dramatically little interest in the European Union or the process of European regional integration. While a healthy cottage industry in EU studies exists among political scientists in the US and Canada, the number of participants at, say, an average American Sociological Association conference who know or care anything about European integration can be counted comfortably on one hand. European sociology went all global, post- and trans-national a long time ago, and its social theorists such as J€urgen Habermas, Ulrich Beck or Gøran Therborn have often found the topic of Europe/EU a congenial playground for their ideas (...).
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.116 | 0.042 |
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".