Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both Marcus (2001) and Jackendoff (2002) have emphasized the importance of finding credible explanations for the occurrence of variables within cognitive representations. Marcus, in particular, has argued that a prevailing form of connectionist modeling, eliminative connectionism, cannot adequately explain crucial forms of human generalization. Eliminative connectionism eschews the use of explicitly represented variables, and the latter, Marcus contends, play an essential role in the forms of generalization that he considers. Recently, van der Velde and de Kamps (2006) proposed a neural blackboard architecture, which they assert to have satisfied the variable representation needs that Marcus and Jackendoff identified. However, this letter argues that closely related variants of Marcus's generalization examples possess variable requirements that are incompatible with the van der Velde and de Kamps approach. Moreover, it is argued here that these newly proposed variants present a severe challenge not only for eliminative connectionism but for all network training methods that require iterative tuning of synaptic strengths. The letter focuses on generalization cases that necessitate either virtually instantaneous creation of variables or very rapid deployment of preexisting variables within highly novel contexts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it