Bibliographic record
Abstract
This study examines year-by-year correlations between Freedom House and Reporters Sans Frontières' (RSF) press freedom scores for countries over a 13-year period (2002–2014). The goal of the study is to test the hypothesis that, further into the age of digital disclosure, as press abuses and harassment of journalists are more widely reported, press freedom ranking systems are gradually becoming more precise and, therefore, correlations between the two indices will strengthen over time. To further assess concurrent validity of the indices, correlations between both indices and scores on the United Nations Human Development Index are also provided. The study also examines changes in the indices' rankings of countries over time within six world regions: the Middle East and North Africa, the Americas, Western Europe, Eastern Europe/Central Asia, Sub-Saharan Africa, and Asia. In so doing, this study adds a degree of understanding to the validity of two press freedom indices that are routinely cited in journalistic reportage and trade journals, as well as many scholarly publications. Results suggest that the two organizations' scoring of press freedom has become significantly more correlated in the years 2002–2014, and the primary cause of the increased agreement is that RSF's ratings became substantially more aligned with Freedom House's scores during this period. Both indices' ratings are significantly correlated with countries' United Nations Human Development Index scores.
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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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".