Bibliographic record
Abstract
Background - Google Wave was touted as the next big communication tool—combining e-mail, social networking, and chat within a single “wave”—with the potential to create a new world for collaboration. Information professionals who are knowledgeable of this tool and its capabilities could become uniquely situated to use it, evaluate it, and teach it. This seemed especially true for those working within Clinical and Translational Science Award (CTSA)-minded institutions, given the promise of interdisciplinary collaboration between investigators and the potential for creating new authorship models. This case study on Google Wave users who are affiliated with CTSA-minded institutions, was designed for and presented at the Evidence-Based Scholarly Communication Conference held by the University of New Mexico Health Sciences Library and Information Center. It provides an early evidence based evaluation of Google Wave’s potential. Methods - Two “waves” were created. The first consisted of five survey questions designed to collect demographic data on the respondents’ roles, a general impression of Wave, the specific tools within Wave that might be useful, and potential collaborators with whom the respondents might use Wave. The second wave was a private, guided discussion on Wave’s collaboration potential. Individuals from CTSA-minded institutions were invited to participate with messages on Twitter, forums, blogs, and electronic mail lists, although there were difficulties reaching out to these institutions as a group. Results - By the conclusion of the study, only a small number of people (n=11, with a viable n=9) had responded to the survey. Given this small result set, it made sense to group the responses by the respondents’ roles (CTSA staff and researchers, support staff, medical librarian, or general public) and to treat them as individual cases. Most of the respondents were librarians and support staff who felt that Wave might have potential for collaboration; there were no CTSA researcher respondents. For the second part of the study, the discussion wave, only one participant explicitly expressed interest in joining. All were invited to join, but there was no participation in the discussion wave at the conclusion of the study. Conclusions -The results of this study implied that Google Wave was not on the forefront of CTSA-minded institutions’ communication strategies. However, it was being used, and it did demonstrate new collaboration and authorship capabilities. Being generally aware of these capabilities may be useful to information professionals who seek to be current and informed regarding developing technology and to those interested in scholarly communication practices. In addition, the difficulties encountered during this case study in attempting to reach out to CTSA-minded institutions raised the question of how members currently communicate with each other as institutions and as individuals. There was a lesson learned in the usefulness of doing case-study research to evaluate new technologies; the cost in terms of time was relatively low, and knowledge about the technology itself was gained while establishing a base level of evidence to potentially build on in the future. Methods: Two “waves” were created. The first consisted of five survey questions designed to collect demographic data on the respondents’ roles, a general impression of Wave, the specific tools within Wave that might be useful, and who the respondents might use Wave to collaborate with. The second wave was a private, guided discussion on Wave’s collaboration potential. Individuals from CTSA-minded institutions were invited to participate from related public waves and by sending out calls for through Twitter, forums, blogs, and e-mail, although there were difficulties reaching out to these institutions as a group. Results: By the conclusion of the study, only a small number of respondents (n=11, with a viable n=9) had taken the survey. Given this small result set, it made sense to group the responses by the respondents’ roles (CTSA staff/researchers, support staff, medical librarian, or general public) and treat them as individual cases. Most of the respondents were librarians and support staff who felt that Wave might have potential for collaboration; there were no CTSA researcher respondents. For the second part of the study, the discussion wave, only one participant explicitly expressed interest in joining. All were invited to join for the sake of numbers, but there was no participation in the discussion wave by the conclusion of the study. Conclusions: The results of this study implied that Google Wave was not on the forefront of CTSA-minded institutions’ communication strategies. However, it was being used and it did demonstrate new collaboration and authorship capabilities; being generally aware of these capabilities may be useful to information professionals who seek to stay on top of developing technology and to those interested in scholarly communication practices. In addition, the difficulties encountered during this case study in attempting to reach out to CTSA-minded institutions raised the question of how members currently communicate with each other as institutions and as individuals. There was a lesson learned in the usefulness of doing case-study research to evaluate new technologies; cost in terms of time is relatively low and knowledge can be gained of the technology itself while establishing a base level of evidence to potentially build on in the future.
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 | 0.026 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".