Discursive Construction of Ingroup and Outgroup Identity in the Bilateral Speech by President Joe Biden and Prime Minister Pedro Sanchez
Bibliographic record
Abstract
This article investigates the speech by President Joe Biden and Prime Minister Pedro Sanchez to determine the discursive strategies used to construct ingroup and outgroup identity, and the functions that these strategies perform. The bilateral speech delivered by Prime Minister Pedro Sanchez and President Joe Biden on June 28th, 2022 serve as the study's data. Extracts from the speech were purposively sampled and subjected to critical analysis using Ruth Wodak's (2009) Discourse Historical Approach. Findings reveal that nomination strategy is linguistically realised through reference, nominalization, material, mental and verbal processes. Nomination identifies the United States, Spain, Ukraine and Russia as the major social actors and categorizes the United States, Spain, and Ukraine as ingroup actors and Russia as outgroup actor. Through predication, the ingroup actors and their actions were metaphorically labelled positively using positive predicates and modifying adjectives. Conversely, the outgroup actor and its actions were framed negatively using negative predicates and modifying adjectives. The actions of the ingroup actors were legitimized using the topos of usefulness and advantage while those of the outgroup actor were delegitimized using the topoi of threat and danger. Perspectivisation, through the linguistic tools of reporting and describing, highlight the overt and conscious stance of ingroup actors as well as their involvement in the discourse while the factuality and validity of their propositions were registered through the intensification strategy linguistically realised through declarative sentences.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".