Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,131 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,016 | 0,025 |
| Science ouverte | 0,003 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».