Postmodern Political Discourse: A Thematic and Linguistic Analysis of Mandela’s Long Walk to Freedom
Bibliographic record
Abstract
The Postmodern wave of democratization and the emphasis on democratic values and right to expression make it imperative that the political discourse be studied with more and full attention. In this regard, one genre that is almost totally ignored in Pakistani context and little attention has been paid to it even at the global level, is autobiography. Autobiography is a special kind of composition in which the author gives a picture of the evolution of the self and its relation with the external world throughout this evolutionary process. The famous political autobiography Long Walk to Freedom by Nelson Mandela is, therefore, selected as the basic unit of analysis. Through content analysis different topics are separated from the original text. These topics are then grouped under different categories of van Dijk’s theory of Political Discourse Analysis (PDA). The exploration and analysis of linguistic devices are also carried out. Besides Van Dijk’s PDA, Huckin’s approach to text and Corpus Linguistics’ quantitative methodology aided the systematic in-depth analysis. Methods of both qualitative and quantitative research have been utilized for this study as the researchers believe that quantification of data along with qualitative description produce reliable results. Findings revealed various linguistic devices are used in abundance. Amongst the most prominent ones are the unique and effective use of the year-statistics, language of the minority regime, Afrikaans, Trilingual combination, dramatic language and listing or cluster of three to stress certain themes like racial discrimination, inequality, poverty, parties, law, justice, separation and history.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".