Bibliographic record
Abstract
This is the second instalment of a special section exploring the pedagogies—classroom and otherwise—associated with mobilities scholarship. As we discussed in the previous introduction (Transfers 13.1/2), the collocation of mobility and pedagogy is by no means a one-way street when it comes to innovation since, in several instances, novel theories and methodologies have emerged directly out of classroom teaching rather than the other way around.1 This dynamic was apparent in the discussions that took place at the first-ever conference dedicated to mobility pedagogy, which took place at Waterloo University, Canada, in 2018 (see Nicholson, 13.1), and is evidenced here in several articles across the two issues.2 As we discussed previously, the field's reputation for innovative methodologies is often the link between research and teaching, and the variety of applications continues to grow. In this special section introduction, we have therefore taken the opportunity to reflect upon some possible new directions for mobilities and pedagogy that take account of not only topical theoretical and political debates but also the pedagogic practices that may, themselves, inspire new research and “real-world” applications. In particular, we share some reflections on the way in which the concept of mobility justice, as first advanced by Mimi Sheller in 2018, lends a new dimension to mobility pedagogies and connects with research and teaching on social justice more broadly.3
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.343 | 0.187 |
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".