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Record W7083312802 · doi:10.22329/uwdj.v1i1.8331

Running a Policython: Creating Epistemic Opportunities for Students

2023· article· en· W7083312802 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueUWill Discover Journal · 2023
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Windsor
KeywordsExperiential learningEvent (particle physics)Student engagementPublic policyHigher educationExperiential education

Abstract

fetched live from OpenAlex

This paper provides a structure for running a policython based on the shared experiences of student leaders and a collaborating faculty member. A policython, like a hackathon, is an experiential learning event where students think critically about public policy, engage in a case study, and share their ideas with community leaders for feedback and consideration. Students are presented with a societal challenge, a short timeline, some open-source documents for review and an opportunity to share their insights and policy solutions. Students learn policymaking strategies during the event, engage in a known public debate, and become epistemic contributors by sharing their ideas. The main goal of the policython is a pedagogical exercise. The capability approach underpins the pedagogical framework of the event. This paper includes author reflections about challenges, opportunities, and successes and recommendations and resources for faculty and student leaders who want to launch a policython.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.575

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 communication0.0010.001
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 head0.082
GPT teacher head0.332
Teacher spread0.250 · 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