Lines of Flight: The Digital Fragmenting of Educational Networks
Bibliographic record
Abstract
With the precipitous changes of the platform formerly known as Twitter, brought on by the change of ownership in late 2022, many networked educators sought, and continue to seek, new digital spaces to continue fostering and developing their digital practice. The authors of this article had all been actively networked on Twitter, and wanted to explore these changes in their professional worlds. As we sought out these spaces we critically began to interrogate our own practices on this platform to gain a deeper understanding of our practice going forward. We’ve approached this exploration through three vectors: an examination of the terms we use to describe movement from platform to platform, digital identity formation and disruption, and building human connection in digital spaces. The findings from our exploration yielded the following conclusions: (1) With regard to metaphors of movement (i.e., “migration”) we leave space open as to which metaphor to use as no metaphor is a perfect fit to explain the complexity of this phenomenon. (2) Digital identity/ies are multifaceted and sometimes place-specific, but some networked affordances seemed to encourage an ever-evolving digital identity more than other spaces. (3) Finally, digital spaces afford us the ability to carve out our own communities from the wider academic community, in the process developing a more owned and voiced identity. However as social media platforms are fleeting, those connections – and identities – are in danger of getting co-opted or deleted as platforms rise and fall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".