Bibliographic record
Abstract
Background: Global digital platforms lower entry barriers for small and medium-sized enterprises (SMEs) by providing access to international customers, payments, and logistics. Yet they also concentrate power in platform governance and algorithmic control, shaping visibility, conversion, and even continued market access.Methods: This review synthesizes research on platform ecosystems, institutional trust, online reputation systems, and cross-border e-commerce governance. We develop an integrative model that links governance design (rules, enforcement, data rights) to trust formation and reputation accumulation, and from there to SME growth trajectories.Results: SMEs grow when platform rules are predictable, enforcement is transparent, and reputation signals are credible. Growth can stall under opaque ranking, abrupt policy shifts, weak dispute resolution, and manipulation of reviews or feedback. We identify governance levers—verification, escrow and dispute resolution, transparency and explainability, data access and portability, and multi-homing compatibility—that shape trust and reputation under cross-border institutional distance. Recent evidence highlights the role of platform governance in seller trust in cross-border contexts, and the vulnerability created by algorithmic ranking opacity.Conclusions: Sustainable SME growth on global platforms is an institutional problem as much as a marketing problem. Resilience depends on rule literacy, disciplined reputation strategy, and governance-aware diversification (multi-homing and off-platform customer development) to reduce exposure to de-ranking and sudden rule changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".