Deconstructing Language Borders through the Hybrid. A Topical Approach to Margaret Atwood’s <i>Dark Lady</i>
Bibliographic record
Abstract
Abstract Starting from the premise that cultures assume myriads of foreign elements, alterities, and differences, this paper analyses a phenomenon that becomes a conscious and an intentional one, namely language hybridity. Our purpose is to give thoughtful attention to certain instances of hybridity perceived at the syntactic, semantic, and lexical levels. Since language users make their choice in any situational context, we witness a great degree of linguistic blending: e. g. the borrowing of words and phrases becomes tied to new ways of making meaning. Additionally, we face a dynamic increase of mixed language registers, styles, and voices that form a complex linguistic repertoire in a literary work. For exemplification, we will analyse Margaret Atwood’s experimentations across genre and linguistic boundaries encountered in her short story Dark Lady , integral part of the short fiction collection Stone Mattress. Nine Wicked Tales (2014). This narrative is characterized by a mixture of heterogeneous elements: hybrid phrases created as a result of borrowing words, elevated language (sprinkled with widely known Latin sayings), and alteration of idioms by one-word substitution. Hybridity becomes a way through which Margaret Atwood deconstructs language borders. In Dark Lady , the Canadian writer shows that hybridity stimulates innovation since the individual is allowed to move freely between spaces of meaning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".