Zanaglasnice u nastavi hrvatskoga jezika – između predodžbe i prakse
Bibliographic record
Abstract
Ovaj se rad bavi položajem zanaglasnica u kontekstu njegove obrade u nastavi hrvatskoga jezika u srednjim Å¡kolama. S obzirom na to da pisani korpus pokazuje velika odstupanja od klasiÄnoga tumaÄenja Wackernagelova pravila (Peti-StantiÄ 2007), htjele smo ovu pojavu istražiti u kontekstu odnosa nastavnika hrvatskoga jezika prema pouÄavanju pravila o položaju zanaglasnica. DosadaÅ¡nji radovi u ovome podruÄju pokazuju razliÄite težnje. Dok veÄina suvremenih gramatika (BabiÄ i dr. 2007, BariÄ i dr. 2005, Težak i BabiÄ 1994) staje u prilog prozodijskome tumaÄenju Wackernagelova pravila, prema kojemu se zanaglasnica uvrÅ¡tava iza prve naglaÅ¡ene rijeÄi u reÄenici, neki suvremeni jezikoslovci (Peti-StantiÄ 2002, 2002a, 2006, 2007) zagovaraju sintaktiÄko tumaÄenje ovoga pravila u hrvatskome jeziku, prema kojemu se zanaglasnice smjeÅ¡taju iza prve naglaÅ¡ene sintaktiÄke jedinice. Ovi drugi svoje tvrdnje podupiru neskladom izmeÄu propisanih pravila i stvarne uporabe, tj. korpusa, a naÅ¡e se istraživanje bavi sliÄnim problemom â odnosom izmeÄu naÄelnih i praktiÄnih elemenata pouÄavanja ovoga pravila. Ispitivanje predodžbe nastavnika o pravilu o položaju zanaglasnica u hrvatskome jeziku te prakse pouÄavanja toga pravila otkrilo je da nastavnici daju prednost prozodijskom tumaÄenju Wackernagelova pravila, no nisu skloni ispravljanju uÄenika koji ga drugaÄije primjenjuju.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".