The Brexit negotiations: If anywhere, where are we heading? “It is complicated”
Bibliographic record
Abstract
What does the British vote to leave the Europen Union (EU) mean and where are the negotiations heading? These are the main questions I discussed with prominent Europeanists during the ECPR General Conference annual EPS debate on September 8, 2017. The word that stuck with me the most in these discussions was “it is complicated.” As of March 2018, nothing has changed in this complexity. The three contributions to this symposium by Sofia Vasilopoulou, Thomas Koenig and Richard Bellamy highlight some aspects of this complexity. First, Prof. Vasilopoulou illustrates that the situation on the British Islands is complicated because the political class and the public opinion have not made up their minds about determining the type of breakup they want. Prof. Koenig highlights that analyzing the Brexit negotiations is complicated because mainstream international relations theories, such as intergovernmentalism, do not allow us to analyze the Brexit negotiations. Prof Bellamy discusses the complexity of holding a second referendum. In this introductory piece, I will contextualize this complexity using the famous Facebook status “it is complicated” as an analogy. I will also embed some of my own opinions with regard to the Brexit negotiations within this opening piece.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 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".