Controversial Search Engine Results: An Exploratory Study of Information Presentation and Use
Bibliographic record
Abstract
The manner in which search results are presented to a user may influence how they come to understand scientific information. Sixty participants were asked to read a mock search engine's result page with the goal of summarizing a science topic for a colleague. The researchers analyzed participants’ summaries for the presence of conflicting or negating information from the mock search results page. Preliminary findings indicate that the way in which a search engine displays results can influence a user's understanding of a controversy, particularly document order and genre, which affected the quality of participants’ written responses. La manière dont les résultats de recherche sont présentés aux utilisateurs peut influencer la façon dont ils interprètent l'information scientifique. On a demandé à soixante participants de lire une page de résultats fictifs d'un moteur de recherche dans le but de résumer un sujet scientifique pour un collègue. Les résultats préliminaires indiquent que la façon dont un moteur de recherche présente les résultats peut influencer compréhension qu’a un utilisateur d'une controverse, en particulier l'ordre des documents et leur genre, qui ont affecté la qualité des résumés produist par les participants.
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 | 0.032 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".