THE SHAPE OF PLATFORM STUDIES: A MULTIDIMENSIONAL METHODOLOGICAL MODEL FOR A VICISSITUDINOUS CONCEPT
Bibliographic record
Abstract
The study of platforms is on the rise in communication studies, science and technology studies (STS), game studies, internet studies, and the study of human-machine communication (HMC). While originally platform studies emerged from hardware studies as an integrated attempt to study the hardware, software, code, marketing, and use of computational technologies—especially, early on, videogame consoles, but never limited to them—its use has been broadened to include the study of software platforms, such as social media sites, and their user affordances, algorithmic decision making, terms of service, background code environments, and embeddedness in neoliberal capitalism: selling user data, acting as advertising mediums, etc. While a fruitful field with much work developed, there is a noticeable dearth of methodological theorising on the topic, even as there are numerous theoretical explorations. How exactly does one $2 platform studies? We propose a multidimensional approach to platform studies, in which work may be located along at least three major axes: computational—sociotechnical, pragmatic—critical, and interpersonal—structural. These three dimensions of platform studies are combinable, provisional, and subject to extension. While the three dimensions offered up for discussion here cannot speak to the entirely of what platform studies $2 or $2 , together and as a starting point these initial three define the shape of platform studies, track the work it has already done, and offer a solid framework and model for future investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.096 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| 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".