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Record W2947729498 · doi:10.15173/ijsap.v3i1.3742

Third space partnerships with students: Becoming educational together

2019· article· en· W2947729498 on OpenAlexvenueno aff
Tom Burns, Sandra Sinfield, Sandra Abegglen

Bibliographic record

VenueInternational Journal for Students as Partners · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipSpace (punctuation)CurriculumWork (physics)PedagogySociologySet (abstract data type)Mathematics educationPolitical sciencePsychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

This case study discusses how we harnessed a University Teaching Fellowship to open a collective third space partnership with “non-traditional” students to enable them to draw on their experiences of transition into higher education and to produce resources designed to help other students find their place, voice, and power at university. We discuss first the “in-between” opportunities of learning development as a “third space profession” that enables us to work in creative partnership with students. We further set the scene by exploring the third space potential of learning development per se and then examine the successful development and administration of a learning development module, Becomingan Educationist, at a medium-sized university in the United Kingdom.We conclude by arguing for third space partnerships not just alongside the curriculum, but in and through the curriculum as well.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0250.015
Scholarly communication0.0150.012
Open science0.0030.037
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0110.002

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 head0.104
GPT teacher head0.553
Teacher spread0.449 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations18
Published2019
Admission routes1
Has abstractyes

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