Evaluating new technologies and organizational structures
Notice bibliographique
Résumé
Welcome to the last issue of IASSIST Quarterly for 2024, IQ 48(4). We are excited to share news of several developments that we have been working on over the last few months: The IASSIST Qualitative Social Science and Humanities Data Interest Group (QSSHDIG) is planning an IASSIST Quarterly special issue dedicated to the complexities of sharing qualitative data. For this special issue, we invite submissions of abstract proposals focused on the ethical challenges, methodological concerns, and labor involved in making qualitative data and research materials publicly available. The full CfP and details on how to submit an abstract can be viewed on the IASSIST Quarterly website: https://iassistquarterly.com/index.php/iassist/announcement/view/7 . The deadline for proposing articles is January 31st (full articles won’t be needed until later). We are delighted to welcome Minglu Wang as a new IQ Editorial Board member (as of October 2024). Minglu is the Research Data Management Librarian in the Open Scholarship Department at York University Libraries, York, Ontario, Canada. Among other qualifications, she brings experience as a member of the Editorial Board for ACRL’s College & Research Libraries (C&RL) (2019–2025), and she led the project group for that Board to investigate a data policy for C&RL. A new feature recently enabled on the OJS platform allows reviewers to link their profile with their ORCID iD. We mentioned last time that this will enable auto-loading of your articles to your ORCID profile, but the other effect is that it provides an opportunity for reviewers to receive credit and be acknowledged for their professional contributions. Note that the credit will merely note that you have served as a reviewer for the IQ—it will not indicate which article(s) you reviewed. Unfortunately, the IQ editorial team had to retract a paper from publication this fall due to plagiarism. The paper titled “Data protection and right to privacy legislation in Kenya” by Mankone, A. M. (2023), was published in IQ, 47(3-4). The full retraction notice can be found here. This new issue of IQ 48(4) presents four excellent papers. The first two evaluate methods to enhance findability of data deposited in data repositories. The subsequent two papers focus on organizational structure and improving organizational workflows. Kokila Jamwal in ”Boosting data findability: The role of AI-enhanced keyword” examines the use of Artificial Intelligece (AI) to supplement keywords that may be missing or inaccurately defined as a method to improve metadata and boost data findability. The author suggests that using this relatively new technology may reduce the time and effort required by data repositories staff for data curation and may enhance data findability and usability. Co-authors Knut Wenig and Xiaoyao Han are examining the findability of data deposited in data repositories that are using DDI metadata standards. Their paper ”State of DDI Cloud” invetigates the availability and the comprehensive element usage of DDI standards across 29 repositories registered on re3data.org. Based on their findings they provide recommendations for various stakeholders including the repositories, Dataverse developers, re3data.org, and the DDI Alliance. The article ”The IPUMS Business Process Model: Instituting a workflow mapping strategy to support archival processes” introduces the IPUMS workflow from external submission of data, harmonization process, documentation, extraction systems, and archival preservation of metadata. Author Diana Magnuson explains the value of instituting this mapping approach, and demonstrates the power of a clear business process model for developing archival goals in an organizational setting in which the archive function is vital but secondary to the main product. In ”Understanding motivations and future needs for data depoists at Korea Social Sciences Data Archive”, authors Hyowon Kim, Do Won Kim and Jungwon Yang evaluate the current data deposit process of the Korea Social Science Data Archive (KOSSDA). The data archive recently transitioned into an idependent researh center under Seoul National Univerity. Using interviews with stakeholders, they identify future needs and suggest a long-term strategy to ensure that the archive meets the needs of the academic community it supports. Wishing you a happy holidays season, and peace, health, and happiness in the New Year. Ofira Schwartz and Michele Hayslett, December 2024
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 | 0,086 | 0,247 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,029 | 0,028 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,006 |
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 ».