Bibliographic record
Abstract
Contrastive specification: an elusive problem It is far from obvious how to decide, for a given phoneme in a given language, which of its features are contrastive and which are not. The problem is made even more elusive by the fact that it does not appear to be difficult. In particular situations we may have intuitions about what the answer must be. But our common-sense intuitions may lead us astray, in this area as in others. Or we may find that we can follow more than one logical chain of reasoning, each of which may appear to be sound, but which lead to different and incompatible conclusions. To give something of the flavour of this problem, both its seeming obviousness and real difficulty, I would like to begin with a quote from Stephen Anderson (1985:96–7). Anderson is illustrating Trubetzkoy's (1939) notion of phonemic content , intended to be the sum of the contrastive properties of a phoneme: ‘If we consider [English] /t/, for example, we can see that this segment is phonologically voiceless (because it is opposed to /d/), non-nasal (because opposed to /n/), dental (because opposed to /p/ and /k/), and a stop (because opposed to /s/ and to /θ/).’ Anderson is not proposing a detailed analysis of English; he is simply illustrating what some of the contrastive features of English /t/ would be in a Trubetzkoyan analysis, and presumably in any analysis of contrast that used these features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".