“Distinctive from What? And for Whom?” Deep Learning-Based Product Distinctiveness, Social Structure, and Third-Party Certifications
Bibliographic record
Abstract
How do producers’ distinctiveness and social structure influence third-party certifications? We argue that producers compete against prior and current competitors, as well as against their past selves. In the context of 153 artists active during a key period of the emergence of modern art (1905-1916), we use a convolutional neural network used in computer vision to extract feature vectors of artworks, and then measure quantitative distance of these artists’ works from canonical reference points. We find that artists are rewarded for distinctiveness from prior and current competitors and their past selves (up to a point). However, the artists’ autonomy to differentiate themselves depends on their position in social structure, which we divide into the supply-side of artist-to-artist networks, and the demand side of artist-to-gallerist networks. Artists with high or low supply-side status receive higher rewards for distinctiveness from current competitors than do artists with middle supply-side status. Artists with higher demand-side status receive higher rewards for distinctiveness from their own past, but lower rewards for distinctiveness from current competitors. These results show that peers strive to constrain each other to conform to positions of gravity within product space, and that market audiences deploy either higher or lower constraints on a producer’s identity depending on the reference point.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".