Campaign Entrepreneurs in Online Collective Action: GetUp! in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, multi-issue, online campaigning organisations have emerged and mobilised citizens on, mostly, progressive issues. For example, MoveOn in the United States is a renowned leader in the field, and similar organisations now exist in the UK, Canada, New Zealand and at the transnational level. In Australia, GetUp!, with over 600,000 members, has become part of mainstream political debate, while also bringing a disruptive social movement approach to online citizen mobilisation. The role of leadership is underexplored in understanding how these organisations discursively construct their actions and successes. This paper argues that online campaigning organisations are increasingly blurring the line between social change, activist politics and the market, and that leaders play a key role in this process. It uses three points of empirical analysis to substantiate this argument. First, the active diffusion of hybrid political repertoires between online campaigning organisations in the USA and Australia consolidates GetUp! within a transnational ‘network forum’. It also demonstrates that that there is a distinct Australian political context based on the history of social democracy shaping progressive social movements and organisational relationships. Second, the career pathways of 23 GetUp! activist campaigners demonstrates the diffusion of personnel between these online campaigning organisations. Further, it highlights the shift some have made from progressive civil society to the creation of new entrepreneurial, market-facing, organisations. Third, qualitatively analysing how three high-profile GetUp! leaders have used both mainstream and social media to successfully promote their ‘story of self’ assists in the development of the concept of ‘campaign entrepreneurs’.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it