Editor's Note/Chairs' Welcome
Bibliographic record
Abstract
Welcome to this issue of the Proceedings of the ACM on Human-Computer Interaction, which will focus on contributions from the research community Computer-Supported Cooperative Work and Social Computing (CSCW). This diverse research community explores how different types of social groups affect, and are affected by, information and communication technology. The topics explored by this community can include social media use, crowdsourcing and micro-work, societal effects of computing, and much more. Like many other HCI communities, CSCW approaches these topics with a broad range of scientific techniques, theoretical perspectives and technology platforms. The call for papers for this issue on CSCW attracted 385 submissions, from Asia, Canada, Australia, Europe, Africa, and the United States. After the first round of reviewing, 207 (54%) papers were invited to the Revise and Resubmit phase. The editorial committee worked hard over August 2017 to arrive at final decisions, with a Virtual Committee meeting held to discuss those papers that needed collective deliberation. In the end, 105 papers (27%) were accepted. This issue exists because of the dedicated volunteer effort of 101 senior editors who served as Associate Chairs (ACs), and 885 expert reviewers to ensure high quality and insightful reviews for all papers in both rounds. Reviewers and committee members were kept constant for papers that submitted to both rounds. Senior members of the editorial group also helped shepherd some papers, reflecting the deep commitment of this research community. We are excited by the compelling and thought-provoking work that resulted in this PACMHCI CSCW issue and look forward to equally high quality submissions for the next submission cycle from this research community in the Spring of 2018. For those interested in this area, this group holds their next annual conference November 3-7, 2018 in New York City's Hudson River (Jersey City). That conference will provide many opportunities to share ideas with other researchers and practitioners from institutions around the world.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 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".