Žurnālista tēls mūsdienu krievu kino
Bibliographic record
Abstract
Bakalaura darbā „Žurnālista tēls mūsdienu krievu kino” autore apskata divas filmas – „Kustībā” («В движении») un „Skārds” («Жесть»). Autore savā darbā izvirzīja hipotēzi, ka žurnālists kino bieži tiek attēlots kā nesaudzīgs repotieris, kurš ir gatavs uz visu informācijas dēļ. Kriminālists vai detektīvs, kas izmeklē kaut kādus skaļus noziegumus vai politiskas intrigas. Un tas ierobežo viņa tēlu skatītāju acīs. Par darba teorētisko bāzi tiek izmantota Alberta Banduras socialās apmācības teorija. Par analīzes metodiku izvēlēta Semiotika – zinātne par zīmēm. Filmu analīze tika veikta izmantojot dažādus analīžu kodus (paņēmienus): verbālais, uzvedības, preču, tehniskais utt. Pētījuma laikā tiek noskaidrots kā žurnālista tēls kino ir stipri kropļots un neatbilst realitātei. Bakalaura darba izstrādes laikā izvirzītā hipotēze guva apstiprinājumu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".