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
Concerns over the toxicity of social media have prompted philosophers to develop a new branch of epistemology focused on the epistemic evaluation of cognitive environments: Environmental Epistemology (though we will mostly use the descriptor, ‘Digital Epistemology’). Traditional epistemology is about the evaluation of persons or groups and this overlooks the evaluation of things and systems in their own right. Epistemic environments – spaces, real and digital, where people interact and communicate – are said to be governed by new specific and general epistemic norms to be philosophically investigated. This paper surveys various proposals within Environmental (or Digital) Epistemology with the aim of clarifying what exactly is being proposed, whether is it worth the attention of philosophers, and how this viewpoint might be defended and applied. Our discussion includes critiques of healthy, neutral, and toxic epistemic environments, environmental resources, epistemic health, pollution, hostility, vulnerability, and flooding. While acknowledging that epistemic environments, including digital media, can inhibit our attempts to reason and understand, we are less confident that this emerging viewpoint has been adequately developed and motivated. Current epistemological frameworks – especially Reliabilist – already have the means to address questions about how to epistemically evaluate informational environments.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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