Innovating through standardization: How Google Leverages the Value of Open Digital Platforms
Bibliographic record
Abstract
The purpose of this paper is to examine how an actor strategically develops and diffuses technology standards that align with innovation trajectories while maintaining a consensus with competitors. We conduct a field study of HTML5 standardization and examine how Google strategically influences the development and diffusion of HTML5 toward their favorable standard trajectories. We show that Google has adopted two strategic policies (integrating outside technologies and avoiding the monetization of technologies) and engaged in two relational practices (forming alliances with browser vendors and engaging developer communities) to realize an open Web application platform on the HTML5 while competing and coordinating with other actors. Google attracts application developers and browser vendors to collaboratively develop HTML5 specifications and HTML5-compatible products and services, which have enriched Google's open Web application strategy. These relational practices were enabled and amplified by non-commercial policies for corresponding web applications and the use of other parties' technologies.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".