Commentary on: Andrei Moldovan's "Denying the antecedent and conditional perfection again"
Bibliographic record
Abstract
Moldovan's paper discusses a model called the "Horn scale," which is intended to illuminate the way that Gricean implicature may perfect a conditional statement, "if p, q," into a biconditional statement, "if and only if p, then q."He addresses various objections to this scale.I find his replies to these objections quite reasonable, although sometimes I find the objections themselves obscure.My feeling, when I look at the Horn Scale, is that the objections he is considering are just the tip of the iceberg: that a lot more objections will come from where those objections are coming from.The reason is that I am reminded of my undergraduate days at Wayne State.I was known in the department as a great counterexampler: people would give me a definition; I would give a counterexample.I was able to do this because I knew Nelson Goodman's Secret: namely, that analytic philosophers have two different notions of logical form: the narrow official notion and the secret robust notion.The official notion does not allow us to distinguish between disjunctive and nondisjunctive propositions, negative and positive propositions, relational properties and non-relational ones, and implicational propositions and non-implicational ones; any given proposition can be written in so many different forms.But we secretly believe-though claiming not to-in a more robust concept, one that allows us to make all of these distinctions.So, when we give a definition, our definition presupposes the robust concept.So it is easy for someone-me-to do a few truth table manipulations and give a counterexample-a Goodman grue-bleen type example-that cannot be answered except by confessing you accept the robust concept.Moldovan's problems are already of this sort to a large extent, and when I look at Horn's Scale, I expect many more such problems to start popping off the wallpaper!Now Horn's Scale supposes we are in a situation where someone wants information about the conditions x such that if x then q, the conditions which imply q.So if there is just one, we say, "if p then q"; if there are two, we say, "if p then q, and "if r then q."If there are three, we say, "if p then q," "if r then q," and "if s then q."And so forth.
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 | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.091 | 0.112 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".