Doing Twitter, Postdevelopmental Pedagogies, and Digital Activism
Bibliographic record
Abstract
In this article, we interrogate how we might manifest early childhood education’s Twitter purview as a space for thinking with postdevelopmental pedagogies. Accordingly, we pay attention to the ethics and politics that shape our Twitter practices, asking how these activate postdevelopmental provocations. In this sense, postdevelopmental pedagogies refer to processes and questions that interrupt the assumptions, objectivity, universalism, and technocratic instrumentalism of child development that so often pervade ECE practice, including much of the #earlychildhoodeducation content. Anchored in the two Twitter accounts that we coordinate, we outline four practices for doing Twitter with postdevelopmental provocations: counterpublics, counter-narratives, and counter-memory, collectivity, and digital feminist activism. We then work through two examples, showing how we draw these practices into our decision making as we craft tweets to activate postdevelopmental questions. We conclude by offering forward questions that educators, pedagogists, researchers, and activists might carry into their own Twitter practices. Keywords: early childhood education, Twitter, postdevelopmental pedagogies, digital activism
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.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".