"Pridodato u prevodu": nematerijalno kulturno nasleđe u prevođenju i primeni evropskih politikâ doživotnog obrazovanja u Republici Srbiji
Bibliographic record
Abstract
Polazeći od noseće pretpostavke istraživačkog programa antropologije javnih i praktičnih politika, prema kojoj zakoni, strategije, vizije i planovi nisu "čisti" pravno-politički akti proceduralnog karaktera, već da "kriju" neke specifične, izabrane ili neintencionalne, antropologiji tradicionalno zanimljive institucionalne izbore iz obilja nematerijalnog kulturnog nasleđa (poput preskripcije identiteta ili standarda normalnosti), iznosimo preliminarne rezultate istraživanja primene evropskih politika doživotnog obrazovanja u Republici Srbiji. Preliminarna analiza "prevođenja" odnosno specifične glokalne kontekstualizacije, recepcije i instrumentalizacije pomenutih politikâ pokazuje da je na snazi praksa prenošenja, pravne i proceduralne transplantacije bez prilagođavanja kontekstu u kojem politikâ treba da bude primenjena. U slučaju neprilagođene politike doživotnog obrazovanja, izostanak prilagođavanja neophodnog da lokalna verzija a) odgovara međunarodno usaglašenim ciljevima i da istovremeno b) zaživi u praksi, objašnjava se na primerima diskrepancije lokalnih pojmova radnog vremena, neformalnog obrazovanja, "odgovarajućeg" životnog doba za obrazovanje s jedne, i pojmova originalno ugrađenih u tu politiku, s druge strane.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".