Bibliographic record
Abstract
Mind 2018, 127(506), 565–584. doi.org/10.1093/mind/fzx013 These amendments are being made to correct typographical and formatting errors, and to correct some implied attributions. Page 565 Abstract, line 9: replace ‘agent-neutral’ with ‘social’ so that it reads ‘social perfectionism’. Page 565, Section 1, paragraph 1, line 2: Replace ‘His answer in the System of Ethics (1798), his answer’ by ‘In the System of Ethics (1978), his answer’ Page 572, Section 3.1, paragraph 4, line 3: Replace ‘We could say, to start with, that dominion over everything…’ by ‘In fact Kosch says that dominion over everything…’ Page 572, Section 3.1, paragraph 4, line 9: Replace ‘This would give us a new formula’ by ‘Following Kosch (2015), this would give us a new formula’. Page 572, Section 3.1, paragraph 4, line 16: Replace ‘alike.’ by ‘alike (Kosch 2015).’ Page 573, Section 3.2, paragraph 2: Reformat ‘Indeterminacy’ and the following sentence so that there is a line space before and after, and so that it is indented and left justified. Page 573, Section 3.2, paragraph 2: Delete indent before ‘Now Wood’. Page 579, Section 5, paragraph 4, line 3: Change ‘it is wholly agent-neutral.’ to ‘it is wholly ‘agent-neutral’ (Kosch 2015).’ This has now been corrected online and in print.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".