Open Collaboration Systems Research Workshop 2015 Report
Bibliographic record
Abstract
Open collaboration systems (OCSs) like Wikipedia, Imgur, Zooniverse, StackExchange, and Reddit have shown that networked communities of volunteer contributors can produce and maintain immense, valuable public resources. A growing body of work within the Computer Supported Cooperative Work (CSCW) research community has come to recognize novel opportunities and challenges that openness brings with it. However, findings from these academic studies do not always permeate the boundaries between scholarship and practice. Furthermore, many important phenomena related to peer production are not yet fully understood. On March 14, 2015 in Vancouver, British Columbia a group of stakeholders from across the OCS research ecosystem came together to discuss the state of open collaboration research and practice, and to develop recommendations for advancing the field of OCS research and improving outcomes for OCS creators, contributors, users, and community organizers. The workshop's purpose was to bring together researchers on both sides of the "data divide" to identify current challenges and opportunities for future research within these research areas, and to develop a preliminary set of requirements for improved resource sharing and collaboration between OC enterprises and academic research institutions. The workshop program focused on characterizing areas of research within the domain of OCSs where partnerships between academic and industry researchers can both increase our scientific understanding of OCSs and also support those systems through research. This report includes findings from the workshop. It is intended to provide scholars, designers, managers, and communities with a set of relevant, current considerations for OCS research and practice agreed upon by leading researchers in the field. This document can serve as a reference point for future research, to direct new work and help justify it. OCS stakeholders may use it to identify knowledge gaps, research opportunities, and design directions, and to argue both the academic merit and practical utility of specific new initiatives that were called for in this workshop.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 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; both teacher heads agree on what is shown here.
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".