Digital News Report (Ireland) 2023
Bibliographic record
Abstract
The global Reuters Digital News Report was commissioned by the Reuters Institute for the Study of Journalism at the University of Oxford.Here in Ireland the researchers from Dublin City University have regular input into the topics surveyed in the report and we analyse the data that are specific to Ireland, and to our selected comparative markets. The global report survey was conducted by YouGov using an online questionnaire at the end of January/beginning of February 2023.In Ireland 2035 people were surveyed using representative quotas for age, gender, region, and educational level.The data were weighted to targets based on census/industry accepted data. A repoll was conducted in Ireland in late March 2023 as YouGov missed a brand in the 'reach numbers offline' .We have only used the repolled data to deliver a number for the specific missing brand.All other numbers are taken from the January/February poll. This year instead of comparing ourselves with the EU market, we now compare ourselves to the same 'Europe' category as in the global report, meaning the 24 countries sampled by YouGov, including countries such as Norway and the UK. For comparison between 2022 and 2023, we have extracted the Europe data rather than the EU category data so that the comparison is straightforward.If data are used from pre-2022, then the comparison involves EU countries. We have also decided to change from a comparison with North America to a comparison with the US by itself.We felt that the North America data were diluted in some topics by having Canada in the equation.Once again for easy comparison with 2022, we have extracted the US data rather than the North American data so that the comparison is straightforward.If data are from pre-2022, then the comparison involves North America. Regarding this type of polling, it should be noted that online samples tend to under-represent the news consumption habits of people who are older and less affluent, meaning online use is typically over-represented and traditional offline use is under-represented.Our data are representative of the 92% online population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".