Porting the political campaign: The NationBuilder platform and the global flows of political technology
Bibliographic record
Abstract
Political parties rely on digital technologies to manage volunteering, fundraising, fieldwork, and data collection. They also need tools to manage web, email, and social media outreach. Increasingly, new political engagement platforms integrate these tasks into one unified system. These platforms pose important questions about the flows of political practices from campaigns to platforms and vice versa as well as across campaigns globally. NationBuilder is a critical case in their study. It is a leading non-partisan platform used in the United States, Canada, the United Kingdom, and Australia. The case of NationBuilder in Canada analyzes how political engagement platforms coordinate the global flows of politics. Through interviews, we find reciprocal influence among developers, party activists, consultants, and the NationBuilder platform. We call this process porting. It results in NationBuilder becoming a more portable global platform in tandem with becoming an imported, hybridized part of a campaign’s digital infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".