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Record W4400503786 · doi:10.54337/nlc.v14i1.8182

Exploring Digital Lifeworlds

2024· article· en· W4400503786 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.310
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it