The Science of Culture? Social Computing, Digital Humanities and Cultural Analytics
Bibliographic record
Abstract
I developed the concept of cultural analytics in 2005 to refer to "the analysis of massive cultural datasets and flows using computational and visualization techniques." In 2007 we established a research lab (Software Studies Initiative, softwarestudies.com) to start working on practical projects. The following are the examples of theoretical and practical questions that are driving our research:What does it mean to represent "culture" by "data"? What are the unique possibilities offered by computational analysis of large cultural data in contrast to qualitative methods used in humanities and social science? How can we use quantitative techniques to study the key cultural form of our era — interactive media? How can we combine computational analysis and visualization of large cultural data with qualitative methods, including "close reading"? (In other words, how does one combine analysis of larger patterns with the analysis of individual artifacts and their details?) How can computational analysis do justice to variability and diversity of cultural artifacts and processes, rather than focusing on the "typical" and "most popular"?Eight years later, the work of our lab has become only a tiny portion of a very large body of research. Thousands of researchers have already published tens of thousands of papers analyzing patterns in massive cultural datasets. First, there are data describing the activity on the most popular social networks (Flickr, Instagram, YouTube, Twitter, etc.), user-created content shared on these networks (tweets, images, video, etc.), and also users' interactions with this content (likes, favorites, reports, comments). Second, researchers have also started to analyze particular professional cultural areas and historical periods, such as website design, fashion photography, twentieth-century popular music, nineteenth-century literature, etc. This work is carried out in two newly developed fields — social computing and digital humanities.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".