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Record W7083584501 · doi:10.11575/prism/50610

Expanding Knowledge Landscapes: Supporting Non-Traditional Theses at UCalgary

2025· other· en· W7083584501 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
FieldMaterials Science
TopicChemical and Physical Properties of Materials
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipMultidisciplinary approachSubject (documents)Point (geometry)Key (lock)Graduate students

Abstract

fetched live from OpenAlex

The prevalence of alternative theses was unknown at our institution. Over two years, a multidisciplinary research team examined examples, support needs, and barriers for graduate students pursuing non-traditional thesis pathways at the University of Calgary. We found that interest in these forms is growing, yet students and faculty often hesitate because processes are undefined, approvals unclear, and institutional supports limited. Our findings point to the importance of early and sustained encouragement, clear messaging that non-traditional theses are accepted, and opportunities to build communities of practice. The library has a key role: connecting students with institutional resources, collaborating with subject librarians to surface examples and engage supervisors, and ensuring repository infrastructure can preserve and share diverse formats. Supporting these needs not only enables innovative scholarship but also broadens what counts as research.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.234
Threshold uncertainty score0.992

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2430.009

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 head0.056
GPT teacher head0.318
Teacher spread0.262 · 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