Bibliographic record
Abstract
This report investigates the working conditions and climate of opinion among science journalists around the world and is part of the activities for commemorating the 20th anniversary of SciDev.Net - the Science and Development Network, which is committed to putting science at the heart of global development. In this survey, the aim is to examine science journalism around the world, considering the background, workload and work ethos of science journalists. The survey was carried out during the COVID-19 pandemic and, as such, they have also included questions investigating journalists' perceptions regarding whether (and how) the pandemic has affected them. The data were collected online between February and May 2021, from professionals working in 77 countries in six world regions: Asia/Pacific, Europe/Russia, Latin America, Northern Africa and Middle East, Sub-Saharan and Southern Africa, and the USA and Canada.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.023 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.307 | 0.007 |
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".