Goal-Based Categorization: Dynamic Classification in the Display Advertising Industry
Bibliographic record
Abstract
Goal-based categories have recently emerged as an alternative perspective to the dominant account of prototypical market categories. However, key questions remain regarding the mechanisms that would enable stable market exchanges to form around ad hoc and idiosyncratic goal-based categories. Thus, we sought to answer the following question: How can goal-based categorization enable stable market transactions? Through an inductive study drawing on industry discourse, participant observation, and interview data from the online advertising industry, we describe the category infrastructure that enables buyers and sellers to engage in market exchanges using goal-based categorization. Three mechanisms are integral to goal-based categorization in market exchanges: dimensioning (establishing a possibility space in which valuation can take place through the identification, addition, and/or deletion of product features), scoping (selecting particular features in the possibility space), and bracketing (excluding certain actors from participating in market transactions). Moreover, the fundamental principle of valuation in goal-based categorization is goal-based attribution, which involves iteratively adding and deleting features to accommodate evolving goals. Our findings suggest novel directions for work on goal-based categorization as an important element of valuation in modern 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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".