Debunked: Data Literacy For Adults Evaluation Report
Notice bibliographique
Résumé
ADAPT is a world-leading Science Foundation Ireland Research Centre for AI-Driven Digital Content Technology. ADAPT has a dedicated team for Education and Public Engagement (EPE), which aims to inspire the Irish public to learn about emerging technologies that enhance engagement in our digital world and to have a voice on the future of this vital area of research. Data is at the heart of ADAPT’s activities, and data literacy is paramount for citizens to critically engage with emerging technologies. The increasing pervasiveness of digital content and technology in our everyday lives means that young people and adults need to have the skills to think critically about data and make informed decisions, simply in order to thrive in our always-connected world. In 2021 - 2022, ADAPT devised and delivered a series of one-off workshops for adults to promote awareness of the importance of the topic and an opportunity to improve their data literacy skills in an interactive, social space. Originally titled DALIDA, the series was launched publicly with the more memorable name ‘Debunked’. Data literacy is a broad term, encompassing media and social media literacy, as well as numerical literacy. Due to COVID-19 restrictions in 2021, Debunked ran as an online workshop series, rather than the in-person programme initially conceived. It was led by ADAPT researcher Dr Christophe Debruyne (who moved to University of Liége during the project) and the ADAPT EPE Team led by Laura Grehan and Project Manager Anne Kearns, with facilitation support from 23 other ADAPT researchers. Debunked also involved collaborators from Trinity College Dublin including Dr Ciarán O’Neill (Ussher Associate Professor in Nineteenth-Century History and former TCD Community Liaison Officer) and Ms. Mary Colclough (Community & Enterprise Engagement Manager). The primary aim of Debunkedwas to help people navigate misinformation, disinformation and malinformation online by improving their data literacy skills through these workshops. This report presents a formative evaluation of the inaugural Debunked series. Data was collected through a pre- and post-survey of workshop attendees, as well as semi-structured interviews with participants, programme team and collaborators. The results indicate that despite operational challenges encountered due to the move online as a result of Covid-19 public health restrictions, the ADAPT team were able to capitalise on strong workshop content developed in consultation with the public. Workshops made excellent use of narrative and storytelling that covered Irish history and memes, as well as print and online media, graphs and statistics. The resulting responses from participants covered a range of emotions, highlighting the strongly affective nature of practical and personal reflection on data literacy
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,054 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,010 | 0,014 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,062 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».