Facebook’s evolution: development of a platform-as-infrastructure
Bibliographic record
Abstract
The purpose of this article is to operationalise an evolutionary perspective on the history of social media and to trace Facebook’s evolution from a social networking site to a “platform-as-infrastructure”. Social media platforms such as Facebook change constantly on the level of their platform architectures, interfaces, governance frameworks, and control mechanisms, all while responding to their larger environments. By examining the evolution of Facebook’s programmability and corporate partnerships, we develop an empirical historical analysis of the platform’s boundary dynamics that ultimately determine its operational scale and scope. Based on our analysis of a unique set of archived primary sources, we discern four main stages in Facebook’s long-term evolution and discuss the interplay between ongoing processes of “platformisation” and “infrastructuralisation”. We argue that these terms foreground complementary aspects of the platform’s efforts in balancing its expansion and adaptability to changing user needs and other “environmental dynamics” without risking its integrations and embedding in other domains, such as advertising, marketing, and publishing. Ultimately, our contribution illustrates how empirical platform histories can denaturalise the current dominant position of social media platforms, such as Facebook, revealing over a decade of incremental evolution rather than revolution.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".