Bibliographic record
Abstract
Social media such as Twitter and Facebook have become a part of our everyday communication networks. As new communication technologies become integrated into our routine practices, higher education is called upon to accommodate these platforms in order to ensure that students are prepared as skilled digital citizens. Studies of social media use in higher education classrooms are finding that these same technologies that are transforming our socio-technical communication channels outside the boundaries of the classroom are failing to live up to that same potential in educational contexts. Technology domestication describes a process by which individuals or groups encounter and appropriate a new technology into their everyday routines by focusing on the social and political meanings that people ascribe to technology as they use it. This study explores the domestication of social media by university faculty who use these tools for their teaching. This paper reports preliminary findings from interviews with six university instructors who report integrating social media tools into their classroom teaching. Semi-structured interviews were analysed according to the elements of the domestication process proposed by Silverstone (2006). Preliminary findings suggest the following themes as social media is appropriated for teaching in higher education classrooms: (1) faculty use social media alongside other more traditional educational technologies such as learning management systems; (2) appropriation of social media relies on a pre-existing technological infrastructure that includes ubiquitous access to the internet; (3) the incorporation of social media was considered carefully as part of a fundamentally student-centred, participatory orientation to teaching; and, (4) faculty in the study used social media extensively in their personal lives first before bringing it into their classrooms. Domestication theory provides a useful lens for exploring how technologies become tamed through use. This notion of taming suggests that not only are technologies shaped through use, but they also shape action. Through interviews, faculty revealed that although social media technologies had become domesticated in their everyday lives, these same technologies were far from tamed in their educational uses. This paper explores the steps faculty are taking with their students to domesticate social media in their higher education classrooms.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".