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Notice bibliographique
Résumé
Networked Learning (NL), originally presented by Goodyear et al. (2004), has recently been reimagined to embrace a richer, more context-sensitive understanding that incorporates the entangled, emergent and “messy” nature of learning (NLEC, Gourlay, Rodríguez-Illera, et al., 2021). Postphenomenology was cited as one of the multiple methodological frameworks relevant to this redefinition. Matthews (in NLEC, Gourlay, Rodríguez-Illera, et al., 2021) recommends postphenomenology for its focus on human-nonhuman mediation and questions of agency in sociotechnical networks. Similarly, Thestrup & Gislev (in NLEC, Gourlay, Rodríguez-Illera, et al., 2021) draw on postphenomenology to reconceive the learning network as a media ecology where “technology, not being neutral, but multistable (Ihde 1990), mediates the perceptions and actions of the participants (Verbeek 2005), and by that co-shapes the space, the connections, and the network” (p. 346). But what is postphenomenology? This workshop will introduce participants to postphenomenology as a philosophy of technology, a theoretical framework, and a pragmatic approach to doing NL research. Postphenomenology emerged from philosopher Don Ihde’s (1975, 1979) early phenomenological investigations of specific technologies being used in everyday life: chalk, eyeglasses, telephones, etc. His inquiries led to several key discoveries including the occurrence of distinct forms of human-technology-world relations (embodiment, hermeneutic, alterity and background) which can be further characterized by their amplification-reduction structure. Today, Ihde’s approach to studying technologies “phenomenologically, i.e., as belonging in different ways to our experience and use” (1993, p. 34) is known as postphenomenology and has evolved into an increasingly popular posthuman form of qualitative inquiry in education and the social sciences (Aagaard, 2016). As a theoretical framework, postphenomenology views technology not as a neutral tool but as an active mediator in shaping and co-constituting human actions, perceptions, and interpretations including interactions with others and their world (Ihde, 1990; Verbeek, 2005). Peter-Paul Verbeek (2005) expanded postphenomenology to include key insights from Actor-Network Theory, drawing especially on the work of Bruno Latour (1992, 2002), and thereby broadening its theoretical reach to include the morality of hybrid beings and the ethical design of things. As an approach to research, postphenomenology allows for in-depth explorations of how digital technologies mediate educational experiences (Aagaard, 2017; Adams & Turville, 2018). It is especially well-suited to studying how technologies shape ethical actions and decisions (Verbeek, 2011, 2023). Through “investigating how technologies help to shape human practices, perceptions, and interpretative frameworks, [postphenomenology] makes visible a moral dimension of technology itself” (Verbeek, 2023, p. 49). Postphenomenology employs a variety of phenomenological and empirically grounded methods to capture the everyday, lived experiences of different technologies including disciplined observation of humans employing specific technologies (Aagaard & Matthiesen, 2016), “interviewing objects” (Adams & Thompson, 2016) and “thing writing” (Adams & Yin, 2017). Here, doing postphenomenology demands an out-of-the-corner-of-one’s-eye attentiveness to everyday life, “an ear for meaning and an eye for materiality” (Aagaard & Matthiesen, 2016, p. 41, emphasis in original). Postphenomenological analysis often begins by first reconstructing “posthuman anecdotes”, that is, descriptions of human-technology-world interactions as they are lived, then subjecting these “reassembled resemblings” (Adams & Thompson, 2016, p. 31) to a set of postphenomenological analytic tools to help untangle how humans and different technologies in use are mutually shaping and co-constituting each other. Analytics include studying breakdowns (e.g., the “broken hammer” strategy), attending to the invitational quality of things, and discerning the spectrum of human-technology-world (HTW) relations (Adams & Turville, 2018; Adams & Thompson, 2016).
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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 | 0,000 | 0,001 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découle