Reviewing, Creativity, and Algorithmic Information Theory
Bibliographic record
Abstract
We connect critical review and analysis of creative objects to a recent domain-independent creativity assessment framework by Mondol and Brown (2021a; 2021b). Reviewing is interesting for at least three reasons. Reviews are time- and space-limited, unlike other tasks. Reviews are a creative task about creative tasks, and that meta-creativity is interesting to consider theoretically. And reviews cause communication and learning; the various actors (the primary creator, the reviewer, and the reader) interact in complex ways. We show how Mondol and Brown’s framework connects to the process of review, and show how topics like summarization, contextualization and learning fit within an algorithmic information theory frame. We also give some interesting examples, such as analysis of conceptual art and concert reviews, as computation tasks. We finish by showing that (as is often true of algorithmic information theory ideas) it is hard to fulfill our objectives with practical systems, due to uncomputability or intractibility issues
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".