Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".