Identity and National Belonging in Ansaruallah’s Political Rhetoric: A Transitivity Analysis
Bibliographic record
Abstract
The present study concerns the application of the transitivity model as an analytical tool in the investigation of ideology in political rhetoric. Specifically, the paper examines the issues of national belonging and the construction of identity in Ansaruallah’s proposals to the National Dialog Conference in 2013. The data for the study is constituted, for considerations of thematic relevance, by the Sa’ada Issue part of the proposals. The objective of the analysis is to find out in what terms Ansarullah’s identity is constructed, how the nation is promoted as a value in Ansaruallah’s political rhetoric, and how important national belonging is in Ansaruallah’s construction of their social identity. In order to address these concerns, the paper analyzes the nominal and verbal constructions in the proposals using the linguistic framework of the transitivity model (Simpson, 1988). Findings from the analysis reveal that Ansaruallah construct themselves as an oppressed group whose members are bound by religion and oppression, rather than by a nationalist bond. Indeed, Ansaruallah distance themselves from Yemen, fellow Yemenis, the national government and from national institutions, and instead express emotional attachment to Sa’ada, which is construed as the homeland, and solidarity with Ansaruallah militants and Sa’ada residents, who are construed as the co-nationals of the homeland.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".