Bibliographic record
Abstract
Abstract Research purpose. There is a growing trend of society believing more and more in public broadcasting and media because they can objectively explain and reflect on what is happening in the world and Latvia. As the pandemic escalates, people are increasingly listening to what professional journalists and experts are saying. The survey organised by the National Media Council (neplpadome.lv, 2021) also shows that trust in public media in Latvia has been growing. However, one important challenge now is handling misinformation and false news, mostly seen on the Internet and social media. To be able to trace and analyse misinformation and false news that might have a significant impact on the population, first, it is important to identify the traditional and digital media as well as other Internet sources that have been frequently used by the population. The purpose of this study is to identify media platforms, including regional media, that are in daily use by the Vidzeme region’s population. Design / Methodology / Approach. A quantitative research method was used in this study, specifically, a survey, which consisted of a questionnaire comprising 18 questions on media use. Overall, 333 respondents living in the Vidzeme region participated in the survey. The questionnaire was designed in collaboration with Alberta College communication students. It included questions on traditional (TV, radio, magazines, newspaper) and digital media (portals, social media) that the population uses to access daily and weekly news. Findings. 1) The most frequently used media are social media (87.5%), while the Internet (85.5%) is considered the most reliable source of information. 2) Regional media continue to be an essential source of information for local communities. Originality / Value / Practical implications. Communication specialists of regional municipalities can use the results of the study to strengthen communication with the population via municipality websites and newspapers, thus, contributing to regional, local, and fast-growing micro-media ecosystem understanding. One limitation of the study is the limited scope of research, which is why the authors are planning to expand the research into the study of other regions of Latvia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".