Pushing LIMITS
Bibliographic record
Abstract
As a pandemic rages and ecosystems around the globe collapse, the LIMITS community---along with the rest of the world---is working to adapt. Some adaptations to adjust to emergencies are easy to imagine. For example, months before in-person conferences were canceled in response to COVID-19, the User Interface Software and Technology (UIST) 2019 conference took significant steps towards hosting a geographically distributed, virtual conference, in part adapting to the global climate emergency. This type of change is conceptually clear, if organizationally challenging. However, many needed changes are conceptually difficult, even in the midst of an existential crisis. It is long-recognized that we need to bridge the separation between scholarly venues and publications that focus on technical aspects of computing systems (i.e., "applied") and those that center social and political aspects of computing systems research and design, particularly when attempting to address complex life-wide problems. Yet, disciplinary crystals (e.g., siloes) remain resistant to change. The authors of this paper contribute to ongoing socio-technical efforts, identifying dominant practices and forces that reinforce the socio-technical divide, and holding up empirical projects that offer promising alternatives.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.014 |
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".