Orienting toward the otherwise in the Twitterverse: Activating pedagogical commitments with pedagogists and Twitter
Bibliographic record
Abstract
This article asks how social media platform engagement, on Twitter in particular, collides with thinking and experimenting with the educational practice of a pedagogist. Beginning with tracing the role of the pedagogist as an emerging figure in early childhood education within the Canadian context, we turn to thinking #BecomingPedagogist, the hashtag used on Twitter by those undertaking work toward responding to and inventing the digital practices of a pedagogist. We situate the labor of a pedagogist on Twitter amid public pedagogies and then follow some contemporary threads of Twitter activism. Then, we offer two “problems” for pedagogists on Twitter: (1) settler moves to innocence through utilizing “beauty” as a primary frame for engaging with tweets from Indigenous people; (2) resisting the pull of individualizing, productivity-oriented neoliberal logics on Twitter. We conclude by offering questions we hope pedagogists will consider and continue to grapple with throughout the collective project of thinking pedagogists’ online practices.
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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 | 0.001 | 0.000 |
| 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".