Revisiting media literacy measurement: Development and validation of 3‐factor media literacy scale
Bibliographic record
Abstract
Abstract Background In this technologically advanced era, media literacy is necessary to effectively evaluate the information and understand various biases inherent in media messages. Several media literacy (ML) tools are available; however, we need generic and objective tools that can be applied to all forms of media messages. Objectives The current study aimed to develop and validate an objective and generalized measure of media literacy based on the previously available tools. This study suggested that the access component should be removed from the media literacy tools as recommended in previous literature. Methods The total of 386 respondents, both males and females, were recruited from different universities in Lahore. The age of the sample ranged from 18 to 25 (M=20.98, SD=2.12), with an approximately equal proportion of males (47%) and females. Results and Conclusions This study proposed a compact Media Literacy Scale (MLS) with 3 constructs: analyze (09 items; α=.76), evaluate (08 items; α=.72), and comprehend (07 items; α =76). This 24 items scale explains 55.4% variance was administered to 386 respondents aged 18 to 30 years (M=20.98, SD=2.12). This developed scale will help assess the baseline level of media literacy in the audience so that in the future, evaluation of the efficacy of media literacy, and media literacy programs could be provided.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".