Content is King: An Analysis of How the Twitter Discourse Surrounding Open Education Unfolded From 2009 to 2016
Bibliographic record
Abstract
<p class="3">Inspired by open educational resources, open pedagogy, and open source software, the openness movement in education has different meanings for different people. In this study, we use Twitter data to examine the discourses surrounding openness as well as the people who participate in discourse around openness. By targeting hashtags related to open education, we gathered the most extensive dataset of historical open education tweets to date (<em>n</em> = 178,304 tweets and 23,061 users) and conducted a mixed methods analysis of openness from 2009 to 2016. Findings show that the diversity of participants has varied somewhat over time and that the discourse has predominantly revolved around open resources, although there are signs that an increase in interest around pedagogy, teaching, and learning is emerging.</p>
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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".