Bibliographic record
Abstract
Old beliefs, new symbols, new faces. In 2013, a small group of German green and left-wing activists, professional campaign NGOs and well-established protectionist organisations set up deceptive communication campaigns against TTIP, the Transatlantic Trade and Investment Partnership between the European Union and the United States. Germany's anti-TTIP NGOs explicitly aimed to take German-centred protests to other European countries. Their reasoning is contradictory and logically inconsistent. Their messages are targeted to serve common sense protectionist demands of generally ill-informed citizens and politicians. Thereby, anti-TTIP communication is based on metaphoric messages and far-fetched myths to effectively evoke citizens' emotions. Together, these groups dominated over 90 percent of online media reporting on TTIP in Germany. Anti-TTIP protest groups in Germany are not only inventive; they are also resourceful. Based on generous public funding and opaque private donations, green and left-wing political parties, political foundations, clerical and environmental groups, and well-established anti-globalisation organisations maintain influential campaign networks. Protest groups' activities are coordinated by a number of former and current green and left-wing politicians and political parties that search for anti-establishment political profiles. As Wallon blockage mentality regarding CETA, the trade and investment agreement between the European Union and Canada, demonstrates, Germany's anti-TTIP groups' attempts to undermine EU trade policy bear the risk of coming to fruition in other Eurpean countries. And they carry the real possibility of depriving EU Member States from new economic opportunities and economic convergence. (...)
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".