Teaching Dimensions based on Cooperative Learning.
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
The problem of how a teacher and a learner can cooperate in the process of learning concepts from examples in order to minimize the required sample size without “coding tricks ” has been widely addressed, yet without achieving teaching and learning protocols that meet what seems intuitively an optimal choice for selecting samples in teaching. We introduce the model of subset teaching sets, based on the idea that both teacher and learner can exploit the assumption that the partner is cooperative. We show how this can reduce the sample size drastically without using coding tricks. For instance, monomials can be taught with only two examples independent of the number of variables. The corresponding variant of the teaching dimension (STD) turns out to be nonmonotonic with respect to subclasses of concept classes. We discuss why this nonmonotonicity might be inherent in optimal cooperative teaching scenarios. Nevertheless, trying to overcome nonmonotonicity, we introduce a second variant, the recursive teaching dimension (RTD), which is monotonic and yields the same positive results for some concept classes, such as the class of all monomials, yet can be arbitrarily worse than the STD.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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