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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".