10.-12. klašu skolēnu medijpratība
Bibliographic record
Abstract
Apjomīgais informācijas daudzums, neierobežotās piekļuves un izmantošanas iespējas, tehnoloģiju attīstība un mediju vides pārmaiņas liek nepārtraukti attīstīt savas kompetences pilnvērtīgam darbam ar medijos pieejamo saturu. Pētījuma mērķis izpētīt Jelgavas 10. - 12. klašu vidusskolēnu medijpratības kompetences un noskaidrot izglītības iestāžu pedagogu viedokli par medijpratību, tās nozīmi. Teorētisko bāzi veido Alberta Banduras sociāli kognitīvā teorija, Ērika Eriksona personības attīstības psihosociālā teorija un UNESCO mediju un informācijpratības kompetenču matrica. Darbā izmantotās datu ieguves metodes – aptauja un ekspertu intervijas. Pēc pētījuma rezultātiem var secināt, ka jaunieši pilnībā neapzinās vairākas medijpratības kompetences, pedagogi izprot medijpratības nozīmi, bet kā vieniem, tā otriem nepieciešama papildus zināšanu apguve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".