L’America secondo Trump: un problema identitario che passa attraverso la geografia
Bibliographic record
Abstract
Baptizing or renaming places has always been a political act of great importance. It is currently evident that Trump’s second presidency is characterised by the use of a strongly volitional communication, which seeks to gain a hold on the collective imagination. It ranges from the restoration of Anglo-Saxon toponyms to changing the name of the Gulf of Mexico, up to the hope of extending the USA to the Panama Canal, Canada and Greenland. It is difficult to understand when we stop at toponymy and when we slip into geopolitics. Toponymy helps us understand the political legacy that President Trump draws from, both in foreign and domestic policy. The objectives, multiple, aim to profoundly and lastingly change the USA. However, everything suggests that the season of globalization entrusted to international institutions (United Nations, WHO, WTO, climate agreements, etc.) is now part of the past.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".