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Record W4410496679 · doi:10.15173/ijsap.v9i1.5939

Showcasing Street Law

2025· article· en· W4410496679 on OpenAlexvenueno aff
Kathryn Saban, Francesca Jackson, Nadia Patel

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLawPolitical science

Abstract

fetched live from OpenAlex

Formative feedback is noted as one of the most useful instruments for improving student learning in higher education (Irons, 2007; Gibbs & Simpson, 2004). This case study is a joint staff-student collaboration in which the authors showcase their innovative and novel approach to staff and students acting in partnership to provide and receive formative feedback when working on group projects. The authors discuss the practicalities of students taking the lead on receiving formative feedback through the introduction of student-led formative feedback team meetings with their tutors. In turn, the authors discuss the student voice regarding the importance of formative feedback, arguing that facilitating a mechanism for students to be actively involved in receiving formative feedback can provide autonomy in learning and can promote opportunities for development of key employment skills. The case study is set in context by demonstrating how this method of giving and receiving formative feedback is utilised in a “Street Law” module delivered at Lancaster University Law School.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.040
GPT teacher head0.560
Teacher spread0.520 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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