Bibliographic record
Abstract
Abstract These days, when we hear the slogan ‘let's make our country great again’ we almost automatically assume the state concerned is the US, and the leader uttering the slogan is President Trump. This article invites readers to explore the discourse and practices through which another national leader is seeking to restore his country's ‘greatness’ and promote national and international security. The leader concerned is France's Emmanuel Macron. Why focus on the French president? Because since his election he has become the most dynamic European leader, on a mission to enhance France's international stature, and to do so via a broader process of protecting and empowering the EU. More broadly, France stands out as a country whose political leadership has long been committed to the goal of playing a global role. As Pernille Rieker reminds us, ‘Since 1945, French foreign policy has been dominated by the explicit ambition of restoring the country's greatness [ la grandeur de la France ], justified in terms of French exceptionalism’. 1 Macron has cast his vision of national/European greatness, security, and international order in opposition to the isolationist, rigidly nationalist visions articulated by his domestic opponents and, internationally, by President Trump. In his view, France and Europe can only be secure if they defeat the illiberal ideas advocated by the increasingly vocal political forces, particularly far-right movements, seeking to undermine the core values and multilateral principles of the post-1945 international order. Under these circumstances, an analysis of Macron's policies and practices of grandeur can help us gain a better understanding of the competition between liberal and illiberal worldviews – a competition that is increasingly pronounced within the Western world.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".