Bibliographic record
Abstract
In the current global scenario, the Internet is increasingly becoming a central informational medium that is transforming the way we learn, teach, and communicate. Social media offers a public platform that allows an exchange of thoughts and ideas through posts, tweets, and comments, albeit with word or character count restrictions. Evidently, creativity cannot be curtailed through content length restrictions. The emergence of a new genre of short-stories called short-short stories and the birth of a new English dialect called Text-speak prove that every cloud indeed has a silver lining. The popularity of social media exchanges signify that technology users have accepted quick social media interactions as a new way of life and have also adjusted their writing to match the content restrictions. Educators and parents are concerned that the attitudes and habits of tech-savvy generation are muddying Standard English as Text-speak is infiltrating students assignments blurring the distinction between formal and informal writing. The phenomenal popularity of short stories that can fit in a tweet or text is an example of how adversity can be turned into an opportunity. Literary purists, however, are concerned that digital literature is shrinking and short-stories are severing their characteristic elements to comply with the restrictions. This paper delineates the impact of technology on daily English writing and literature.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".