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Record W3092503081 · doi:10.1080/10691316.2020.1830908

From students to authors: Fostering student content creation with Scalar

2020· article· en· W3092503081 on OpenAlex
Marcela Isuster

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.

Bibliographic record

VenueCollege & Undergraduate Libraries · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsMcGill University
Fundersnot available
KeywordsScholarshipCreativityComputer scienceScalar (mathematics)World Wide WebClass (philosophy)Library instructionMultimediaMathematics educationPsychologyInformation literacyMathematicsPolitical science

Abstract

fetched live from OpenAlex

The research paper, an established method of assessment, is not always the best choice of assignments. Its structured format can stifle creativity and inhibit other methods of constructing knowledge. To address these concerns, a liaison librarian partnered with faculty to have students create Scalar projects. Scalar is a free and open-source publishing platform that facilitates the creation of multimedia digital scholarship, making it a great platform to produce engaging interactive visual essays that can transcend academia and foster student content creation. This case study describes several iterations of Scalar assignments that were implemented in seven courses in six disciplines, with four different instruction methods. Variations in the effectiveness of these methods demonstrated the need for interactive in-class instruction and the benefits of the librarian-faculty collaboration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.829
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.263
Teacher spread0.148 · 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