Linking Metanarrative: Lexical Content in Preeti Shenoy’s A Hundred Little Flames
Bibliographic record
Abstract
The Postmodern metanarrative abbreviates the relation between entrap the texts that are wreaked by incidents, quotes, allusions, translation and so on. The aim of the study spotlights on the postmodern tendency of metanarrative in Preeti Shenoy’s selected text, A Hundred Little Flames. Preeti Shenoy is a multifarious postmodern writer. The term metanarrative is linked with dialect in the texts. The features of metanarrative are dialect, incident, way of narration and allusion etc. Jean Francois Lyotard is one among notable theorist of metanarrative. The present study has adopted only Lyotard’s metanarrative theory. The methodology of the study splits into three metanarrative concepts – narration towards knowledge, narration towards dialect and narration towards nostalgia. Lyotard proposed these three diverse concepts in his famous book The Postmodern Condition. These metanarrative concepts are adapted to the present study, and the authors have investigated the metanarrative elements in the select text of Shenoy. The results of the study are evaluated with other studies under postmodern metanarrative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".