Meaning Perpetually Deferred: A Derridaean Study of Sam Shepard’s True West
Bibliographic record
Abstract
This article aims at reading Sam Shepard’s True West from deconstructive point of view. Derrida with coining the word “Differance”, consisting of the words to “defer” and to “differ”, disturbs the presence of meaning, contending that no stable meaning exists. Meaning is forever fallen into the trap of “differance”, causing the meaning to defer, that is, the signified is always deferred and we are just dealing with play of signifiers. Moreover, he believes that in each set of binary oppositions, the two sides of opposition not only add to each other but also take the place of each other and thus supplement each other. This is in fact what happens in True West. Characters’ identities have unstable nature. Each character changes their identity from one type of personality to another one, thus plunging themselves into finding floating identities. In addition, the characters supplement each other; they need each other to be completed, as two sides of opposition, without having priority over each other. Therefore, what fills the space of the play is the indeterminacy regarding Derrida’s ideas of supplement and “differance” propelling the characters into having unstable and changing identity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".