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Record W7010599955

Introduction: Mobilities and Pedagogy : Moving Forwards

2023· article· en· W7010599955 on OpenAlexaboutno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMobilitiesVariety (cybernetics)PoliticsDimension (graph theory)Economic JusticeReputationSection (typography)Social justice
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.026
GPT teacher head0.294
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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