Perspective-based search: a new paradigm for bursting the information bubble
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
Nowadays, individuals heavily rely on search engines for seeking information. The presence of information bubbles (filter bubbles and echo chambers) can threaten the effectiveness of these systems in providing unbiased information and damage healthy civic discourse and open-minded deliberation. In this paper, we propose a new paradigm for search that aims at mitigating the information bubble in the search. The paradigm, which we call perspective-based search (PBS), is based on the intuition that in a fair search the user should not be limited to the results corresponding to a specific perspective of the search topic. Briefly, in PBS, different perspectives of the search topic are identified and presented to the user and the user can select a perspective for the search results. In this paper, we focus on the paradigm itself, why it is an appropriate solution, and how it differs from other solutions. We raise new questions and call for research on the paradigm and on providing solutions for implementing its required components. We do not aim at providing any specific implementation for it, although we provide some hints on implementing it. We also provide a survey of the related concepts and methods and discuss their differences with PBS.
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 | 0.000 | 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