Bibliographic record
Abstract
This paper explores how Large Language Models (LLMs) foster the homogenization of both style and content and how this contributes to the epistemic marginalization of underrepresented groups. Utilizing standpoint theory, the paper examines how biased datasets in LLMs perpetuate testimonial and hermeneutical injustices and restrict diverse perspectives. The core argument is that LLMs diminish what Jose Medina calls “epistemic friction,” which is essential for challenging prevailing worldviews and identifying gaps within standard perspectives, as further articulated by Miranda Fricker (Medina 2013, 25). This reduction fosters echo chambers, diminishes critical engagement, and enhances communicative complacency. AI smooths over communicative disagreements, thereby reducing opportunities for clarification and knowledge generation. The paper emphasizes the need for enhanced critical literacy and human mediation in AI communication to preserve diverse voices. By advocating for critical engagement with AI outputs, this analysis aims to address potential biases and injustices and ensures a more inclusive technological landscape. It underscores the importance of maintaining distinct voices amid rapid technological advancements and calls for greater efforts to preserve the epistemic richness that diverse perspectives bring to society.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.493 | 0.243 |
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".