Digital Activism, Federal Elections in Canada, and Dissent: Organic Intellectuals Challenging Stephen Harper (2008-2015)
Bibliographic record
Abstract
Grounded in a neo-Gramscian framework, this research project considers the citizen challenge to Stephen Harper’s Conservative government over three Canadian federal elections – 2008, 2011, and 2015, stepping outside of traditional election studies to analyze electoral mobilizations from the perspectives of digital activists and the prospects for building online social movements. It extends Gramsci’s theory of the organic intellectual as activist and educator to contemporary times by problematizing the concept of the digital organic intellectual, emphasizing the importance of digital platforms for activism and the tool of critical pedagogical humour as a creative response to rising authoritarianism under Harper. The specific political moment of the Harper reign aligns with the evolution of social media through its early days in 2008, to shifting political and digital paradigms in 2015 leading to his defeat. While engaging in a critical analysis and recognizing the limitations of social media sites, this study draws on hopeful frameworks that interrogate positive aspects of online networking, extending Gramsci’s ideas for social transformation into the 21st century. Gramsci is brought into conversation with authoritarianism (Harper) in the digital age and thus Gramscian concepts are re-positioned and elevated within the literature. Through semi-structured interviews with 21 virtual organic intellectuals, one focus group and a digital mapping, this purposive qualitative study considers the rich and diverse activism of the political moment. It provides an opportunity to track and understand electoral digital activism and explore online social movement building that embraces voices from the margins, putting Gramsci’s understanding of power and social change into conversation with Harper’s controlling authoritarian hegemony. The spotlight on this specific historical epoch offers insight into activism and mobilization. No matter the platform, the people, or the practice, these activists who were undeniably ‘in the trenches’ from early days of 2008 and beyond, played a critical role in developing a dissenting voice that challenged Harper’s increasingly authoritarian agenda. The digital electoral activist work of these organic intellectuals during the 2008, 2011 and 2015 federal elections in Canada has been largely invisible and anonymous. This dissertation with the help of a neo-Gramscian framework is their moment to shine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".