DEVELOPMENT OF MEDIA COMPETENCE AT WORK IN THE USA AND CANADA
Bibliographic record
Abstract
In the article the importance of education of media literate individuals for conscious use of informational content has been stressed. The topicality of media competence development in working population explained by the fact that a modern employee should have digital and media skills which enhance every employee’s competitiveness and economic efficiency of the industry. The author distinguishes types of office workers’ professional activity that require digital and media skills. The analysis of the most efficient practices in the implementation of on-the job education in the USA and Canada has been carried out. The conclusion about the necessity of employees’ media education arranged by employers, means of company further development as well as staff development and retaining has been made. Taking into consideration the future economic profit and social benefits for companies and communities from digitally and media literate employees, the long term strategy of digital and media literacy improvement of different categories of population was worked out aimed at increasing productivity and competitiveness in the USA and Canada. The main aim of educational programs (development of basic skills for the use of digital and media resources, development of media competence for improvement of CEOs’ leadership qualities as well as creation of own media production) has been revealed. It has been stressed that governments support companies providing their workers on-the-job digital and media education by means of different tax benefits. The author draws attention to the popularity of such programs in American and Canadian societies, their economic profit and positive influence on the society. Key words: media competence, an employee, on-the-job development, corporate clients, non government organizations, USA & Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".