Alberta Bela personības un romāna "Cilvēki laivās" apguve literatūras stundās 12. klasē
Bibliographic record
Abstract
Diplomdarba „Alberta Bela personības un romāna „Cilvēki laivās” apguve literatūras stundās 12. klasei” mērķis ir izstrādāt metodiskās sistēmas variantu, kurš veicinātu Alberta Bela personības un romāna „Cilvēki laivās” apguvi, aicinot pārdomāt valodas un tradīciju nozīmīgumu katras tautas pastāvēšanā. Pētāmās problēmas aktualitāti mūsdienās nosaka jauniešu nevērīgā attieksme pret tautu, valodu un tradīciju daudzveidību pasaulē. Skolēnus ir jāmudina saskatīt katras tautas un valodas vērtība, jārosina apzināties tradīciju nozīmīgumu savā un savu pēcteču dzīvē. Diplomdarbam ir ievads, piecas nodaļas ar apakšnodaļām, kā arī izmantotās literatūras un avotu saraksts, secinājumi un 27 pielikumi. Izstrādātās metodiskās sistēmas efektivitāte pārbaudīta pedagoģiskā izmēģinājuma laikā. Diplomdarbs iesakāms vidusskolas latviešu valodas un literatūras skolotājiem, kā arī citiem interesentiem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".