বল সহতযর ঐতহসকয়ন ও লকসহতযর সলতমম (Historicization of Bangla Literature with Reference to Geneaology of Folklore)
Bibliographic record
Abstract
কাকে বলব ‘লোক’, বাংলা সাহিতযের মলধারায় তাদের জায়গা আদৌ হবে কিনা, এই বিষয়ে দিনেশচনদর সেন এবং সকমার সেন, বাংলা সাহিতযের দই মসত ঐতিহাসিকের পরসপরবিরোধী বিচারই এই পরবনধের মল দরষটবয বিষয়। এই পরবনধের বকতবয, বসততপকষে, তাদের ‘হযাবিটাস’(বরদিও-র কথা ধার করেই যদি বলা যায়) থেকে তারা যেভাবে বাংলাকে একটা ‘কালপনিক গোষঠী’র আদলে কলপনা করছেন, সেটাই ছায়া ফেলছে তাদের বাংলা সাহিতযের ঐতিহাসিকায়ন সংকরানত বোঝাপড়া এবং তার পরচেষটার ওপর। সমসত পরসপরবিরোধিতা সততবেও, (বাংলা) সাহিতয আর ‘লোক’এর উচচাবচটা তারা সমসবরে মেনে নিচছেন। পাশচাতয এপিসটেমোলজিকাল কাঠামো অনসরণ করে ধরেই নিচছেন যে এটা ‘এপরায়োরাই’ বা বিচারের উরদধে।This paper shows how Dineshchandra and Sukumar Sen, two towering historians of Bangla literature, contradicted in their assessment of what they purportedly called 'folk' elements and in their opinion of whether it should be included in the canon of Bangla literature. The paper argues that what they envisaged the ‘Bengali' as an 'imagined community’ from their respective ‘habitus’, to borrow Bourdieu’s evocative phrase, is reflected in their respective understandings of and endeavors to historicize Bangla literature. Despite all contradictions, what they seem to unanimously agree on is the hierarchy between ‘folk’ and (Bangla) literature which they assumed as an apriori taking cues from Western epistemological models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".