Making Snowflakes Like Stocks: Stretching, Bending, and Positioning to Make Financial Market Analogies Work in Online Advertising
Bibliographic record
Abstract
Analogies to financial markets have proven powerful in establishing novel or potentially controversial business concepts, even in contexts that deviate significantly from financial markets. This phenomenon challenges theory that suggests analogies work best when elements from a source and target domain map closely to each other. To develop a theory that explains how organizations make initially imperfect analogies “work,” we use a case study of online advertising exchanges, a market-inspired model for buying and selling online advertising space. We find that as organizations stretch an initially misfitting exchange analogy from financial markets to online advertising, they iteratively bend their activities in superficial, structural, and generative ways to match the analogy and position themselves for advantage in the new space being created. Whereas prior studies emphasize shared cognition about familiar domains as the reason why analogies work, our study offers a dynamic account in which stretching, bending, and positioning combine to not only establish the financial market analogy but also subtly change the understanding of markets.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".