Bibliographic record
Abstract
In any collaborative system, there are both symmetries and asymmetries present in the design of the technology and in the ways that technology is appropriated. Yet media space research tends to focus more on supporting and fostering the symmetries than the asymmetries. Throughout more than 20 years of media space research, the pursuit of increased symmetry, whether achieved through technical or social means, has been a recurrent theme. The research literature on the use of contemporary awareness systems, in contrast, displays little if any of this emphasis on symmetrical use; indeed, this body of research occasionally highlights the perceived value of asymmetry. In this paper, we unpack the different forms of asymmetry present in both media spaces and contemporary awareness systems. We argue that just as asymmetry has been demonstrated to have value in contemporary awareness systems, so might asymmetry have value in media spaces and in other CSCW systems, more generally. To illustrate, we present a media space that emphasizes and embodies multiple forms of asymmetry and does so in response to the needs of a particular work context.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".