Discourse and Power in George Bernard Shaw’s Pygmalion
Bibliographic record
Abstract
George Bernard Shaw’s Pygmalion addresses the discourse of education (linguistic retraining in particular) and its interrelationship with other discourses, such as class, and the transformation of individual and social self. It also deals with the dynamics of teacher-student power relationship in the context of education discourse. Believing that education should produce humane and responsible citizens instead of docile slaves, Shaw displays the evils of an incompetent education system. This article explores the discourse of education, its effects on other discourses - particularly that of class - and the knowledge and power it produces with emphasis on Foucault’s theories about power, knowledge, and discourse. In addition to the Foucauldian conceptualization of discourse, linguistic discourse analysis (conversational analysis) is also applied to examine the link between the language use and the modality of power relations in Pygmalion. The aim is to display how education discourse functions through disciplinary productive power and gives rise to a kind of social knowledge. Shaw’s play, it is argued, intimates that an education incommensurate with socio-cultural factors could probably empower the marginal social subjects but it would also displace them, rather than truly promote them, socially. Key words: Bernard Shaw; Pygmalion; Education; Michel Foucault; Knowledge; Discourse; Power
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".