Bibliographic record
Abstract
There has been much discussion recently of what has been labeled the “Brown-Boghossian-McKinsey”, “Brown-McKinsey” or sometimes just “McKinsey” arguments for the incompatibility of externalism and self-knowledge. However, while the three author’s arguments have been treated as interchangeable, they are not identical. In particular, Brown’s and Boghossian’s arguments have a fairly serious flaw that cannot so easily be attributed to McKinsey. In what follows, I’ll (1) present a version of the ‘received’ “Brown-Boghossian-McKinsey” argument, (2) outline what I take to be the most serious objection to it, (3) explain why this sort of objection does not seem, or do not seem immediately, to tell against McKinsey’s argument, and (4) suggest a number of alternative responses that might apply to McKinsey as well. The “Brown-Boghossian-McKinsey” (BB) argument against the compatibly of Externalism and Self-Knowledge is correctly attributed to Jessica Brown, and Paul Boghossian, and it runs something like this:
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.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".