Who can speak for whom?: struggles over representation during the Charlottetown referendum campaign
Bibliographic record
Abstract
In this study, I undertake a discourse analysis of struggles over representation as they were manifested in the Charlottetown referendum campaign. I utilize transcripts taken during the campaign derived from the CBC news programs The National, The Journal, and Sunday Report as well as from The CTV News. The issue of (im-)partiality provides the analytical focus for this study. Who can legitimately speak on behalf of whom, or, to what extent do individuals have a particular voice which places limitations on whom they can represent? On the one hand, underlying what I call the ‘universalistic’ discourse is the premise that human beings can act in an impartial manner so that all individuals have the capacity to speak or act in the interests of all other individuals regardless of the group(s) to which they belong. On the other hand, a competing discourse based on group-difference’ maintains that all representatives express partial voices depending on their group-based characteristics. I argue that the universalistic discourse was hegemonic in the transcripts but, at the same time, the group-difference discourse was successful at articulating powerful counter-hegemonic resistance. Ironically, the universalistic discourse was hegemonic despite widespread assumptions of partiality on the basis of province, region, language, and Aboriginality. This was possible because the universalistic discourse subsumed territorial notions of partiality within itself. In contrast, I argue that assumptions of Aboriginal partiality will likely diffuse themselves to other categories, beginning with gender, in the future. I also describe the strategies used by the competing discourses to undermine one another. The universalistic discourse successfully portrayed the group-difference discourse as an inversion to a dangerous apartheid-style society where individuals were forced to exist within group-based categories. The group-difference discourse used the strategy of anomaly to demonstrate that individuals were inevitably categorized in the universalistic discourse; impartiality was a facade for a highly-partial ruling class. In examining these strategies, I demonstrate that the group-difference discourse justified its own position by making assumptions about the operation of power and dominance in society. Thus, impartiality was impossible not for the post-modern reason that inherent differences make representation highly problematic, but because power relations hinder the ability of representatives to act in a truly impartial manner.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".