Bibliographic record
Abstract
Philosophy has long wrongfully imprisoned reasoning within the isolated chambers of the individual mind. This book shatters this confinement, laying foundations of a groundbreaking framework that conceptualizes reasoning as protocol-articulated action governed by socially shared norms and unfolding across diverse sites of processing. While logicians portray reasoning as inhabiting an abstract system of rules applied to propositions, this book argues that this portrayal distorts the truth of the matter. Reasoning in the Wild is founded on the principle that the relation of logical consequence is best understood as a relation between concrete acts, not between abstract propositions inhabiting inference systems. This is the zeroeth principle of logic. Consonant with this principle, the book proceeds to illuminate: the vital concept of the common mind—the shared understandings and ways of thinking that exist within a community; how reasoning is inherently social, a public work that unfolds across various sites of processing, operating on principles of trust; that communication shapes action within the context of the common mind, but subject to manipulation that can contribute to polarization; that public sentiment is a powerful, macrosocial force shaped by interlocking processes of shared understanding and strategic communication; and finally, how a disjointed common mind can result from persistent false narratives. The book provocatively considers the role of generative AI in either exacerbating or ameliorating this condition. Reasoning in the Wild is a keen philosophical intervention on a broad range of interconnected topics around the public works of reasoning, for scholars and graduate students in philosophy and the social sciences, particularly the sciences of mind, logic, the social world and communication. It promises to reshape fundamentally our understanding of how we reason, not in terms of isolated mental activity but within the rich tapestry of human connection and public life.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".