Bibliographic record
Abstract
In linguistics, “neologism” is a newly coined word or phrase which may or may not belong to existing vocabulary. We create or use different words in different contexts. Sometimes, these words lose their original meaning and are interpreted for some other meaning. Sometimes such words are used as a derogatory indication to someone or something, we refer to them as “ hate neologism ”. There are several such neologisms coined on different occasions, especially during elections in India. It is rising every day even though the election commission of India has imposed strict restrictions. It is noticed that during the election campaigning many such hate neologisms are coined targeted to an individual, opponent parties, or some communities. For example- “পাপ্পু / Pappu”, “দিদি / Didi”, “পিসি / Pishi”, “ভাইপ / Bhaipo”, “ফেকু / Feku” etc. Each of these words has its specific literal meaning, but in the election campaigning, it is used sarcastically. In this article, we have proposed an automated approach to finding the various hate neologism in the election context in Bengali language.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".