Examining Readers’ Evaluations of Objectivity and Bias in News Discourse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Readers’ objectivity and bias evaluations of news texts were investigated in order to better understand the process by which readers make these kinds of judgments and the evidence on which they base them. Readers were primed to evaluate news texts for objectivity and bias, and their selections and metacommentary were analyzed. Readers detected bias in passages with stance markers, and detected objectivity in those lacking stance markers. In their metacommentary, readers tended to characterize objective texts as lacking purpose, or having a merely descriptive or expository purpose, and biased texts as exhibiting explicit interpretive or argumentative purposes. Unlike studies that locate objectivity or bias in news texts, or test it by asking about the fidelity of texts to their sources, our study examined the evaluations of readers in their interactions with texts. It shows how objectivity and bias evaluations are a multiply determined part of a communication dynamic rather than a fixed quality of a text.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it