Bibliographic record
Abstract
Europeanized public spheres affect politics. This broad claim is accepted by all of the contributors to this book, even while they disagree on other issues: the precise extent of Europeanization in this area (a little or a lot); the way to measure public spheres (claims, frame, or discourse analysis); where precisely to look for such spheres (among elites and the quality media or a more bottom-up, civil-society view); and – finally – the politics being affected by them (party-political cleavages or identity politics). My purpose here is not to adjudicate among these disputes; the book’s opening chapter does an excellent job of highlighting and justifying them while persuasively demonstrating the common ground shared by all (see Chapter 1). Thus, the collection is a state-of-the-art treatment of the subject matter – European public spheres – in the best sense of that phrase: telling the reader what we have learned but also where our knowledge is incomplete or disputed. My chapter continues with this last point, making three arguments about these loose ends. First, the workings of public spheres are ultimately claims about the ability of language and communication to shape politics. Elsewhere, however, such linguistic approaches have been supplemented by analysts arguing that institutions, power, and practice are important as well; a similar move seems absent in work on public spheres (see Chapter 8). The result is incomplete arguments – for example, on the relationship of public spheres to changes in European identity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".