Bibliographic record
Abstract
Despite the need to better understand the changing dynamics between the ongoing political regionalization processes and the re-scaling of nation-states, at least in Europe, updated and timely research that responds to these challenges fueled by data-driven societies and the algorithmic revolution invigorated by an uneven establishment of borders remains scant and ambiguous. Nations, regardless of the spatial boundary by which we define them, matter as much as political borders and account for algorithmic disruption. Hence, this paper explores these new cartographies from the regional studies perspective by presenting the city-region as a pivotal term amidst a wide range of challenges for cities, regions, and nation-states. The Basque Country, as a small, stateless, city-regionalized European nation, is presented as a case study, focusing on its transitional techno-political and city-regional metaphor called ‘Euskal Hiria’ (Basque City). The paper examines five standpoints in the understanding of this notion as well as three potential drivers (metropolitanization, devolution, and the right to decide) that will further determine its future position amidst Spain, France and the EU. The paper explores the concept of Basque City in the context of the attempts by small states (such as Estonia and Singapore) and small, stateless city-regionalized nations (such as Catalonia, Flanders, and Quebec) to modify their governmental logics and devolve powers through blockchain technologies, thus enabling their interactions directly with citizens by setting up new city-regional and techno-political patterns that this paper terms ‘Algorithmic Nations’.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".