Re-programming the Mind through Logic. The Social Role of Logic in Positivism and Lieberâs Mits, Wits and Logic
Bibliographic record
Abstract
This essay on the social history of logic instruction considers the programmatic writings of Carnap/Neurath, but especially in the widely read book by Lillian Lieber, Mits, Wits and Logic (1947), where Mits is the man in the street and Wits the woman in the street. In the ‘pre-Toulmin’ days it was seriously argued that the intense study of formal logic would create a more rational frame of mind and have many beneficial effects upon the social and political life. It arose from the conviction that most metaphysical conundrums, religious and political problems and even fanaticism had their root in the irrationality of ordinary discourse, which had to be replaced by the more logical ‘ideal language’ of Principia Mathematica. The enthusiastic promotion of formal logic occurred at a time when it was widely thought that minds could be ‘made over’, ‘reprogrammed’ by proper intervention. This stands in stark contrast to the motivation for teaching informal logic and critical thinking, as it becomes apparent in a 1981 exchange between Ralph Johnson and Gerald Massey in Teaching Philosophy. Most of this essay focuses on Lillian Lieber, an earnest and enthusiastic advocate of the cause of formal logic, and on the reasons for the widespread conviction that, for the sake of peace and social harmony, formal logic should, if possible, be taught to every man, woman and child
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".