Bibliographic record
Abstract
In this paper, I provide a general response to the points made in the commentaries. One of the major questions probed is whether gradient, variable output is diagnostic of gradient mental representations. I argue that this is not necessarily the case, and look to aspects of the learning theory (such as input processing, restructuring, and cue reweighting), as well as the architecture of the phonology/phonetics interface to account for such variation. I argue for a conservative, incremental restructuring process as the basis of the transition theory of L n developmental paths. I reiterate the nature of the projection problem at various levels of the prosodic hierarchy when it comes to the input underdetermining the cues to abstract, algebraic phonological constituent labels. The question of whether L n grammars are consistent with the structural properties of natural languages (i.e. constrained by UG) is discussed. I conclude with a presentation of the idea that linguistic representations can be considered wavelike superstates. This has the potential of capturing what has been described as the fuzziness of representations, as well as the benefit of unifying our treatment of mental and physical objects.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.084 | 0.028 |
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".