A Linguistic Discursive Analysis of Techno-Colonialism Through the Post-cyberpunk Literature
Bibliographic record
Abstract
Technological advancement has made the world a complex arena of day to day transforming phenomenon. In such a complex and technologically progressive world nothing is static instead things have become technology oriented. The socio-historical phenomena like orientalism and imperialism are also not free from technological progress. Similarly, literature of the contemporary times has become Postmodernist for it now aims to represent the current human experiences. The quality of the Postmodernist literature is to represent and dismantle the socio-cultural constructions that use to perpetuate control and power. The objective of this research is twofold; it has projected the world of technological progress and innovation through the analysis of the selected Post-cyberpunk novel Accelerando (2005) by Charles Stross, The Windup Girl (2009) by Paolo Bacigalupi and The Rapture of the Nerds (2012) by Cory Doctorow and Charles Stross. Socio-Cognitive analysis (van Dijk, 2008) has projected the linguistic discursive analysis of techno-colonialism in order to answer the research questions. The study has also introduced Post-cyberpunk as the genre of Postmodernist twenty-first century literature. The findings of the research have suggested that the selected Post-cyberpunk novels have not only represented techno-colonialism but they have also characterized the impact and influence of the techno-colonizers throughout the world.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| 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".