Electronic marketplace definition and classification: literature review and clarifications
Bibliographic record
Abstract
The definitions and classifications of any new phenomenon build a strong foundation for further research. Although research on electronic marketplaces (EMs) has proliferated in recent years, related definitions and classifications are still confusing and misleading. The purpose of this paper is to perform a review of the EM literature, and to clarify and explain published information about electronic marketplaces. For EM definitions, we emphasise (1) the difference between EMs as governance structures and as business models, and (2) EMs at different levels of centralisation. For EM classifications, we summarise nine of the most commonly mentioned classifications, and examine the differences and correlations among them. By doing so, potential confusion and common misunderstanding about the different EM definitions and classifications are clarified.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.030 | 0.043 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".