The Uses of X/Twitter by Members of the TESOL Community
Bibliographic record
Abstract
A lack of dialogue and collaboration between researchers and practitioners has been recognized in the field of second language education. Social media platforms such as X/Twitter have potential for connecting professionals in the teaching of English to speakers of other languages (TESOL) community and supporting professional learning and research; however, studies of TESOL professionals’ uses of X/Twitter have only examined posts/tweets from a limited number of communities marked by hashtags/ keywords. This study identifies 23 hashtags relevant to TESOL instruction for adults in the Canadian context and used them as search parameters to extract a data set of 4,833 posts/tweets. Eighty-two North American university professors who had published in the field of TESOL, were selected and searched for on X/Twitter. Upon locating 15 X/Twitter professor accounts, all 272 posts/tweets posted over the one-year period, were extracted. Two content analyses were conducted to infer the purpose of the posts/ tweets and identify the hashtags used by the professors. Results reveal considerable variation in the professors’ and other TESOL community members’ uses of X/Twitter and suggest that the two groups participate in rather separate X/Twitter communities. Recommendations for maximizing X/Twitter as a tool for professional learning and research and fostering the research-practice link are provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".