Notice bibliographique
Résumé
Editors' Foreword:Digital Tools and Networks Donald Crafton (bio) and Susan Ohmer (bio) Media scholars, specialists, and those of us who engage with the materiality of film, television, and old and new media in our lives have been aware for a long time of the digitization of our endeavors. Some of us use rapidly evolving tools to teach and write; some of us use them mainly to access sources; some of us innovate those tools and curate the sources; some of us are still, figuratively speaking, trying to figure out the TV remote. This special issue of The Moving Image addresses all these constituencies. We are fortunate to have two recognized specialists in the theory and practice of digital humanities (DH) to advise us. Dimitrios Latsis teaches at the School of Image Arts of Ryerson University in Toronto. He received his PhD in film studies from the University of Iowa and completed a postdoctoral fellowship in visual data curation at the Internet Archive. Grazia Ingravalle holds a PhD in film studies from the University of St. Andrews and has been awarded a Leverhulme Early Career Fellowship (2017–20) to work on her new research project about colonial archival films and contemporary archival exhibition practices. Currently she is working on a book, Curating Film History: Film Museums and Archives in the Age of New Media. We commend our guest editors for achieving their goal of assembling "a primer on some of the most significant recent developments in the fields of film history, archiving, and preservation." It is an important contribution, especially because, at this early stage of what will be a vast transformation of a discipline, few of us can say with certainty what is coming. The experts they have selected give us a few glimpses of present and future possibilities. And it's an exciting, generally optimistic view. We want to point out, though, as devil's advocate, that the discussions and [End Page vii] debates will not stop here. Arguments and observations about the unfolding crises that digitization is triggering have been aired in this journal over the past five years and will no doubt continue. The present issue gives us a concentrated snapshot of the current situation. The rapid and pervasive shift to digital technologies, the conversion of standard resources (such as trade journals and card catalogs) into searchable databases and scans, and the introduction of cutting-edge analytic tools and digital source materials described in several of our articles already have upended the field. Not only how we do our work but, moreover, the most fundamental ways of thinking about and planning how we go about doing our business have been altered. Most of the writing on DH assumes, and several studies in this issue affirm, that these digital tools and networks are progressive and welcome additions to the archivist's and scholar's arsenal. That being said, in a couple contributions, we detect traces of a dystopian cloud that hangs over the conversation. Is digitization an improvement when the new tools make the old ones and their resources obsolete or inaccessible? Or do the machines displace something quintessentially personal and insightful about archiving and scholarship? Or are online networks substituting for the physical labor of hired staff and academic researchers? Now, these questions in various iterations have persisted since the Industrial Revolution to be sure, but the speed and pervasive nature of current changes have redrawn the critical discussion in sharp focus. Technology is never pure, and the technological changes in our field predictably have an inescapable political context. Our digital world is shifting resources globally (boosting the economies of some nations and forcing reorganization in those of others). It is affecting young people's career choices and established workers' security. And it is changing the ways in which those institutions that produce, study, and present media are interacting with their constituencies, their publics, and—fundamentally—their revenue sources. In the United States—but our case is not unique—politically motivated deep cuts are being inflicted on the public funding of research, education, and cultural initiatives. Will the digitization tools and networks exemplified by the innovative and vital projects cited in this issue be nipped in...
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».