آموزش سواد رسانهای در فضای مجازی (مقایسه وبسایت مدیااسمارتس کانادا و وبسایت سواد رسانهای ایران)
Bibliographic record
Abstract
In different countries, media literacy education methods and success rates differ. For example, Canada, with its profound history in media literacy education, is the most successful country in the world, but despite widespread importance of media in Iran, media literacy education in this country is relatively new.The present study compared performance of a model country in teaching media literacy (Canada) with a country new on this way (Iran). Therefore, the performance of Canadian Media Awareness Network that provides media literacy education via “Media Smarts Website” and Iranian “Media Literacy Website”, the only Persian website providing media literacy education, were compared using “content analysis" method. The main results are as follows: majority of the contents in Iranian Website were "informative" and "alarming", but most of the contacts in Canadian Website had features of a real “educational sources. Canadian Website developed educational contents using the theories proposed by media literacy experts; While Iranian website has not used these valuable sources.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.033 |
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".