Professors and Scholars as Experts: Problem Setting and Methodological Considerations for the Examination of Newspaper Articles
Bibliographic record
Abstract
Even if new media like wikis obtain some attention, classical media (radio, television, newspapers) still are important sources of information for the informed public, e.g. the public that is interested in what is happening in his/her environment and the world, and wants to understand events and situations.Authority still comes with specific media that have kept on establishing their credibility.But journalists themselves also need reliable sources of information, and they require regularly University professors and similar Scholars in renowned institutions.This allows them to include precisions and richer background in their papers to inform and help understand complex phenomena.After presenting the general context and the problem setting of this research both as a knowledge question and as a social issue, a few short articles will be examined in detail to see how this works in specific examples.
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 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 | 0.364 | 0.477 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".