Constructing Trade : A Critical Discourse Analysis of CETA Narratives Among Swedish Firms, Institutions, and Media
Bibliographic record
Abstract
This study examines how Swedish firms, trade organizations, government bodies, and media discursively construct the Comprehensive Economic and Trade Agreement (CETA) and the Canadian business environment. Drawing on Fairclough’s three-dimensional Critical Discourse Analysis (CDA) and institutional theory, particularly Scott’s (1995) regulative, normative, and cognitive pillars, the study investigates how institutional meaning is communicated, legitimized, or contested. Empirically, it analyzes texts from 87 Swedish firms with operations in Canada, alongside statements from trade groups, government reports, and media coverage. The findings reveal a significant discursive asymmetry: while government and trade organizations actively construct narratives of legitimacy, partnership, and regulatory alignment, firms remain largely silent, mentioning CETA only rarely, and often only on informal platforms like LinkedIn. This silence is interpreted as discursive decoupling, suggesting that firms symbolically benefit from institutional structures without publicly reinforcing them. Partnership emerges as a key institutional strategy, invoked across actor groups to frame alignment and cooperation. By integrating institutional theory and discourse analysis, the study contributes to our understanding of how trade agreements are implemented and narrated into existence, and how their legitimacy depends as much on who speaks as on what is said.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".