The nonword repetition task as a procedure for assessing phonological development at the preschool age : the possibility of specific language impairment discrimination in the Serbian language
Bibliographic record
Abstract
Specifični jezički poremećaj (SJP) je heterogeni razvojni jezički poremećaj koji podrazumeva značajan deficit u jezičkoj sposobnosti (sa posebnim slabostima u domenu fonologije i morfo-sintakse) koji se ne može pripisati oštećenju sluha, niskoj neverbalnoj inteligenciji, neurološkim oštećenjima, emocionalnoj i socijalnoj deprivaciji i drugim poznatim faktorima. Zadatak ponavljanja pseudoreči koji se sastoji u izlaganju i trenutnom ponavljanju izmišljenih reči (pseudoreči) i ispituje sposobnost fonološke reprodukcije je, prema nalazima istraživanja u drugim jezicima, obećavajući psiholingvistički marker za SJP iz razloga što deca sa SJP konzistentno imaju slabiji uspeh na ovom zadatku u odnosu na svoje vršnjake tipičnog razvoja (TR). Cilj istraživanja prikazanog u ovoj disertaciji je da se primenom zadatka ponavljanja pseudoreči, konstruisanih u skladu sa karakteristikama srpskog jezika, ispita sposobnost fonološke reprodukcije TR i SJP dece predškolskog i ranog školskog uzrasta koja usvajaju srpski jezik i da se utvrde razvojno diskriminativni i parametri diskriminativni za SJP koji će poslužiti za konstrukciju testa. Ovakav test bi omogućio procenu fonološkog razvoja kod dece koja usvajaju srpski jezik i, uz dodatne procene stručnjaka u kliničkoj praksi, omogućio diskriminaciju SJP i, potencijalno, drugih govorno-jezičkih teškoća kod dece...
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".