STREAMING SMALL SHARED SPACES: EXPLORING THE CONNECTEDNESS OF THE PHYSICAL SPACES OF MICROSTREAMERS AND THEIR AUDIENCE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines how microstreamers either intentionally or unintentionally share their intimate physical spaces with audiences. While most streaming research focuses on larger and/or monetized professional streamers, there is emerging research on ‘microstreaming’—streams whose audiences are often as low as single digits—and their importance as smaller, more intimate spaces. Given their casual nature, microstreamers are much less likely to have invested in professional level equipment, or to have dedicated streaming-specific areas of their homes. Some scholars have argued that streaming from intimate spaces such as bedrooms can be considered performative, yet our current research questions the broad applicability of such findings, especially with respect to microstreamers. One way to understand these shared spaces is through the lens of place. Streaming represents an event in which the barriers around the “first place” are intentionally removed, and spectatorship invited. Professional streamers navigate this knowingly and intentionally whereas microstreamers may not – the shared spaces of microstreamers can be understood as an unintentional “leaking” of one’s privately held backstage, made available for consumption by unknown others. In our observations of microstreamers, we note that 1) their environments are multi-purpose, unstaged, and shared with others, 2) these others often interrupt or modify the content of the stream in ways that leverage the space in generating increased authenticity, and 3) these streamers mimic more professionalized streams in amateur ways that again produce a sense of realism and endearment. These elements coalesce to provide a unique sense of authenticity and charm to microstreamer content.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it