Having in Mind: The Philosophy of Keith Donnelan, edited by Joseph Almog and Paolo Leonardi
Bibliographic record
Abstract
This is a fascinating book in which eight well-qualified scholars focus, explore, explain, and discuss Keith Donnellan’s main influential ideas. To people familiar with Donnellan’s work, it should not come as a surprise that the notion of having in mind (with Russell’s theory of acquaintance)—so central to Donnellan’s (1966) famous distinction between the referential and attributive use of descriptions (‘Reference and Definite Descriptions’, Philosophical Review, 75 (1966), pp. 281–304)—plays central role. Yet, other important ideas of Donnellan are discussed and explored: for example, his view on empty terms and negative existentials (‘Speaking of Nothing’, Philosophical Review, 83 (1974), pp. 3–31) and the necessary a posteriori (‘Kripke and Putnam on Natural Kind Terms’, in Knowledge and Mind, ed. Carl Ginet and Sydney Shoemaker, Oxford: Oxford University Press, 1983, pp. 84–104). One of the recurring themes of this collection is Donnellan’s contribution to what John Perry (Ch. 1) characterizes as American referential realism , that is, the so-called direct reference move that, in the seventies, so forcefully introduced doubts about the Fregean (descriptivist) semantics framework. Donnellan was, no doubt, one of the main protagonists of this move and most of the chapters highlight the importance of Donnellan’s views in the shaping of philosophy of language in general, and the theory of reference, in particular. It is, therefore, not astonishing to see most contributions (Joseph Almog Ch. 9, Andrea Bianchi Ch. 5, Antonio Capuano Ch. 2, David Kaplan Ch. 8, and Howard Wettstein Ch. 6) focusing on what ‘direct’ in ‘direct reference’ means and, finally, on what is the starting building block for a theory (or picture, as some would say) of reference and semantics in general. Two main (competing) views come to the forum: the one defended by those who, following Donnellan’s seminal work, consider as the starting point the cognitive notion of having someone/thing in mind ; and the one offered by those more inspired by Saul Kripke ( Naming and Necessity, Cambridge Mass: Harvard Univeristy Press, 1980) and the Kaplan of ‘Demonstratives’ (in Themes from Kaplan, ed. Joseph Almog, John Perry, and Howard Wettstein, Oxford: Oxford University Press, 1977, pp. 481–63) and ‘Afterthoughts’ (in Themes from Kaplan, ed. Joseph Almog, John Perry, and Howard Wettstein, Oxford: Oxford University Press, 1989, 565–614), who favour an instrumentalist, social model. In what follows I will try my best to summarize the main points the authors make in their contributions. I will then end with the main view that emerges from the book and my general impression of it.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".