Bibliographic record
Abstract
Abstract During his campaign, Barack Obama inspired record numbers of Americans to donate their time and money to his electoral efforts. Now that the campaign is over, can Obama sustain this civic engagement as he begins to govern? This paper examines the possibilities for sustaining Obama's electoral mobilization, introducing new data from fieldwork conducted from September 2008 to Election Day 2008 in Atlanta, Georgia; Chicago, Illinois; and Charlotte, North Carolina. The data include staff interviews and observations of canvassing, rallies, and other get-out-the-vote efforts of the local Obama and McCain campaigns, the local Democratic and Republican Parties, and various nonprofit groups in each city. Based on these data it is clear that each city was characterized by excitement and heightened activity; however, the number of activities and the strength of the grassroots organization varied across the cities according to national electoral imperatives in ways that should affect the potential for future mobilization. As such, sustaining the mobilization of Obama's supporters faces several hurdles: campaign staff and volunteers in many cities were drawn from outside the community, tensions arose between local grassroots organizations and the campaign over resources and issue focus, and the extremely large amounts of money needed to finance the mobilization were not distributed evenly across cities and states.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".