Bibliographic record
Abstract
Speaking at the Congress of African People in September 1970, Amiri Baraka said, “In Newark, when we greet each other on the streets, we say, ‘what time is it?’ We always say, ‘It’s nation time!’ Nationalism is about land and nation, a way of life trying to free itself.” National identity and nationhood are too often easily dismissed as retrograde populism or racist exclusion. Instead, they need to be understood as a key part of a vision of globalization that holds the imperatives of diversity and solidarity in a delicate balance. Jerry White offers a defence of the nation based on the assumption that struggles for national identity have often unfolded in ways that should be familiar to those who defend the political standpoint of the progressive left. Having evolved into something that a wide variety of actors have sought to defend, nations can also serve as a defence against the homogenizing forces of globalization and as havens of diversity in opposition to more singularly minded forms of affiliation. It’s Nation Time is structured as a series of specific case studies that speak to theories of nation and their historical and cultural manifestations. It includes examples as varied as Black nationalism, Simone Weil’s hopes for a postwar France, the first independence period of Georgia, the Bollywood cinema of Nehru-era India, and small or stateless nations such as New Zealand, Quebec, Ireland, Catalonia, the Métis, the Mohawk, and the Inuit to argue that nationalism is a social form that has much potential and life in it. Broadly internationalist but also deeply insightful about the particular cultures and politics of small nations, It’s Nation Time defends an idea of nation, and a form of nationalism that are rooted in the potential for diversity, flexibility, and progressive politics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".