Scientific Realism for the Contemporary Materialist
Bibliographic record
Abstract
In this extended note we discuss some trends and developments in the philosophy of science and related questions that are potentially of substantial interest to Marxists. The benefits of a fruitful dialogue between scientific realism and dialectical materialism, which would assist in elaborating a Marxist view of science, have been elegantly alluded to in a remarkable essay by the American philosopher, Roy Wood Sellars.1 In a brief sympathetic survey 2 of dialectical materialism written in 1944, Sellars credits Marxism as the only intellectual force that steadfastly stood up to positivism for several decades despite its origins outside academia and its relative lack of academic acceptance for several decades. However with the growth of realist trends in academic philosophy itself, Sellars writes of the positive contribution that could be made to advance the insight available in Marxist classics such as Lenin’s Materialism and Empirio-Criticism by utilizing the technical advances of philosophy. Sellars ’ own pursuit of this engagement in subsequent years resulted in the volume of essays titled Philosophy for the Future: The Quest of Modern Materialism3 edited by him together with V.J. McGill and Marvin Farber. This fascinating volume contains not only essays by Sellars and his son, the (better known) philosopher Wilfrid Sellars, but also those of a host of
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".