The role of teacher's self-efficacy, sense of responsibility towards students and empathy in teaching about prejudice
Bibliographic record
Abstract
Cilj ovog istraživanja bio je ispitati koliko često nastavnici koriste pojedine mehanizme podučavanja o predrasudama te vidjeti u kakvom je odnosu čestina korištenih mehanizama s njihovim osjećajem osobne odgovornosti prema učenicima, nastavničkom samoefikasnošću i empatijom. Podaci su prikupljeni online upitnikom te je u istraživanju sudjelovalo 269 nastavnika razredne i predmetne nastave iz osnovnih i srednjih škola. Za potrebe istraživanja konstruiran je Upitnik mehanizama podučavanja o predrasudama koji mjeri dva faktora – Podučavanje o predrasudama i Suradnja s drugima, evaluacija programa i planiranje aktivnosti, a osim njega korištena je Skala nastavničke samoefikasnosti, Skala osobne odgovornosti, Toronto skala empatije te pitanja o sociodemografskim karakteristikama sudionika. Rezultati su pokazali veliku učestalost korištenja mehanizama podučavanja o predrasudama kod svih nastavnika. Dobivena su dva faktora tih mehanizama. Nastavnici su značajno češće koristili mehanizme Podučavanja o predrasudama od mehanizama Suradnje s drugima, evaluacije programa i planiranja aktivnosti. Veća nastavnička samoefikasnost bila je značajno povezana s češćim korištenjem oba mehanizma, dok je manja empatička neosjetljivost bila značajno povezana s češćim korištenjem mehanizama Podučavanja o predrasudama. Veći osjećaj odgovornosti za motivaciju učenika i manji osjećaj odgovornosti za samo podučavanje bili su značajno povezan s češćim korištenjem mehanizama Suradnje s drugima, evaluacije programa i planiranja aktivnosti. S obzirom da je ova tema dosad neistražena dobiveni podaci vrlo su korisni jer mogu poslužiti kao temelj za daljnja istraživanja.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".