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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".