“Notre avenir, c’est nous qui le choisissons! #PolQC”: Twitter, political discourse, and the modern Québécois nationalist identity
Bibliographic record
Abstract
Québec’s struggle for independence has come to define its identity and political agendas. To the Québécois, the quote “Je me souviens,” reflecting the Québécois nationalist discourse (cf. Heller & Labrie, 2004) entails a past in which an anglophone minority marginalized the francophone majority, limiting their political, social, cultural, and even linguistic rights (Oakes & Warren, 2009). Québécois political parties, including the Bloc Québécois (BQ), the Parti Québécois (PQ), and the Coalition Avenir Québec (CAQ), continue to invoke elements of this discourse through their media communications to frame their perspectives on the modern Québécois identity (Oakes & Warren, 2009), political ideologies (van Dijk, 2002), and platforms (Parmelee & Bichard, 2012), including Twitter, a micro-blogging tool for mass communication (Zappavigna, 2013). We formed a corpus of these parties’ Twitter feeds from just before the January 2017 mosque attacks in Québec City until one month after the attacks. We performed a political discourse analysis (van Dijk, 1997) on the tweets, using the program MaxQDA. Our analysis will focus on the following questions: How do the PQ, the BQ, and the CAQ express their political identities and purposes through tweets? Which political ideologies are represented in these parties’ tweets; what are points of convergence and divergence among them? Initial findings suggest that all three parties invoke symbolic elements of the Québécois identity in their tweets, identifying the Québécois as victims of the Canadian federal government’s aggression. The majority of the BQ’s and PQ’s tweets advocate for Québec’s political independence on both the federal and the international stage, while the CAQ focuses on Québec’s domestic issues. Each party also portrays itself as the protector of the interests of other minoritized populations. Thus, while the parties’ tweets maintain historicity with traditional nationalist discourse, they also indicate a new discourse valorizing diversity and globalization. Summary This poster presents a political discourse analysis of 3 Québécois political parties’ tweets, focusing on the parties’ expression of political and cultural identity. While each party maintains historicity, identifying the Québécois as victims of the Canadian federal government; the tweets also indicate a new discourse valorizing diversity and globalization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".