Bibliographic record
Abstract
Currently, there is widespread media coverage about the problems with 'fake news' that appears in social media, but the effects of biased information that appears in search engine results is also increasing. The authors argue that the search engine results page (SERP) exposes three important types of bias: source bias, algorithmic bias, and cognitive bias. To explore the relationship between these three types of bias, we conducted a mixed methods study with sixty participants (plus fourteen in a pilot to make a total of seventy-four participants). Within a library setting, participants were provided with mock search engine pages that presented order-controlled sources on a science controversy. Participants were then asked to rank the sources' usefulness and then summarize the controversy. We found that participants ranked the usefulness of sources depending on its presentation within a SERP. In turn, this also influenced how the participants summarized the topic. We attribute the differences in the participants' writings to the cognitive biases that affect a user's judgment when selecting sources on a SERP. We identify four main cognitive biases that a SERP can evoke in students: Priming, Anchoring, Framing, and the Availability Heuristic. While policing information quality is a quixotic task, changes can be made to both SERPs and a user's decision-making when selecting sources. As bias emerges both on the system side and the user side of search, we suggest a two-fold solution is required to address these challenges.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".