Evaluating Creativity Through the Degrees of Solidity of Its Assessment: A Relational Approach
Bibliographic record
Abstract
Abstract In this paper, we introduce a new approach to creativity assessment. Arguably, one of the main obstacles to creativity assessment is that creativity criteria are likely to change depending on what is assessed and who is making the assessment. We argue that we might be able to solve this problem by adopting a relational ontology, i.e., an ontology according to which beings of the world acquire their properties by relating to other beings. First, we present the main consequences of this ontological approach for creativity assessment: (a) Accounting for the creativity of a given object involves retracing the beings (including criteria) that relate it to its alleged creativity; (b) One can assess the creativity of this object by looking at the number of beings that substantiate this relation, i.e., by looking at what we call the “degree of solidity” of the relation; (c) One can thus account for the specificity of various forms of creativity and, at the same time, compare them in terms of solidity. Building on these ontological assumptions, we then present a new assessment technique, the Objection Counting Technique, before putting it to the test using an excerpt taken from a naturally occurring brainstorming session.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".