Bibliographic record
Abstract
Brave spaces can be understood to be anything from everyday life to spaces with ground rules and an agreement to have challenging conversations. Brave spaces are sometimes assumed to be at odds with free speech. However, since hate speech discourages the speech of those targeted, brave spaces may provide more freedom of speech overall. Reality storytelling shows, in which ordinary people share personal stories, often offer an intentionally or unintentionally brave space and encourage mostly uncensored speech. For this study, the author attended reality storytelling shows across the United States and interviewed participants of these shows, seeking to answer the question: How do reality storytelling shows establish and hold a brave space? Interview transcripts, field notes, and documents were analyzed through qualitative coding, and memos were made available to the public through a blog. Some shows state ground rules along the lines of “no hate speech” for storytellers and audience members and express a willingness to remove offenders. Even shows with no rules do not tolerate hate speech when it happens. In practice, those who transgress are not always removed. Unintentional offenders may be “called in.” Although educational, the process remains uncomfortable. Lessons from reality storytelling that may translate into other areas include these actions: leaders must be willing to take action to hold the space, the community should be explicit about consequences for transgressing norms, and all participants should truly understand how they might be called upon to be brave.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".