A cross-cultural and cross-media comparative analysis
Bibliographic record
Abstract
Media ratings, or content classification, systems have long sought to provide audiences and consumers with a standardized set of guidelines for making informed decisions about the media they watch, play, read, or listen to. Many of these systems focus on identifying themes, imagery and other forms of content deemed sensitive or even harmful to specific potential audience members (mostly children) within specific cultural, political, and historical contexts. The Media Ratings Project sought to critically examine the current use and enforcement of a US industry-based system for rating video games (ESRB) in five Canadian provinces, through a broader comparison of media ratings across the provinces and across national borders (Canada/US, US/EU). This report describes our key findings, which demonstrate consistencies but also important differences between age-based classification systems applied in these different regions, which raise important questions about the role of localized cultural norms and biases in the rating process, especially relating to so-called “controversial” and foreign-made media. We point to deep discrepancies between dominant age-based classification systems and children’s literacy, development/maturity, and broader cultural rights, as an additional area of concern and future inquiry. This report makes the case that there is a clear need for Canada to reconsider its existing approach to video game classification and regulation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".