An interpretive discourse network analysis of post-pandemic economic recovery across EU institutions
Bibliographic record
Abstract
Post-COVID economic recovery agendas emphasise health, sustainability, and resilience. However, how to make economies more health-promoting – and how the relationship between economy and health is best governed – is contestable and normative. This article offers an interpretive use of Discourse Network Analysis to explore the ideas underlying the EU’s economic recovery discourse and the place of health within it. It analyses how documents published in 2020 by various EU institutions talk about health and about economic recovery, shedding light on the relationship between ideas on these topics, and how they form a multifaceted discourse. We suggest that the discourse on economic recovery is underpinned by three ‘idea clusters’ that represent facets of the overarching discourse: ‘Economic and Monetary Union’, ‘Social Europe’, and ‘European Health Union’. We show how socioeconomic ideas, largely from the ‘Social Europe’ cluster, along with health security, are the main bridges that hold the discourse together by argumentatively connecting economic recovery and health. We also highlight that, except for the European Central Bank, idea clusters do not reflect specific institutions, but that all clusters feature in parts of institutions, underscoring that it is important not to treat institutions as monoliths, but to unpack the nuances present within them.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".