Bibliographic record
Abstract
Abstract Better social media is possible. Move Slowly and Build Bridges is the story of activists, software developers, artists, and everyday people who have built the fediverse, a noncentralized alternative social media system. Unlike big tech corporations like Facebook, TikTok, or X, the fediverse is comprised of thousands of small, independent communities who use a Web protocol to communicate with one another. These small communities govern themselves and moderate content at the human scale—compare that to Facebook and X, which try to moderate global conversations. And the fediverse isn’t built in order to gather user data and sell attention to marketers—it’s a more privacy-respecting social media alternative. The most notable part of the fediverse is Mastodon. Founded in 2016, Mastodon was positioned as an alternative to Twitter. Like Twitter (or X), Mastodon members can post, like, share, and connect with one another across the world. Unlike Twitter/X, Mastodon can be completely under the control of its members, from how it’s run to its underlying software. Making a noncentralized, ethically run social media system isn’t easy. The people building the fediverse have faced long hours, burnout, angry debates, and, worst of all, bigotry, death threats, and discrimination. They face constant, nagging doubts: Can we really do this? Can noncentralized social media survive in a world that is used to corporate social media? Can we—all of us—have our own social media? As Move Slowly and Build Bridges shows us, the answer is yes, but it’s going to take a struggle.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.022 | 0.050 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.102 | 0.054 |
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".