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Record W3095524024 · doi:10.5206/elip.v3i1.8567

Practice Makes Perfect

2020· article· en· W3095524024 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEmerging Library & Information Perspectives · 2020
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsWestern University
Fundersnot available
KeywordsExperiential learningInstitutionField (mathematics)Professional developmentPedagogyWork (physics)SociologyMedical educationPsychologyLibrary scienceComputer scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Co-op placements are vital components of an LIS edcucation. Co-op programs allow students to gain relevant work experience, apply their theoretical knowledge of librarianship in the field, and identify new areas for professional development; however, one aspect is often overlooked in these programs. LIS co-op students can expand their experiential learning by proposing and pursuing new projects in the field, which will enhance their overall education and support their institution and profession at the same time. In this article, I will reflect on my co-op experience with the Collections and Content team at the University of Guelph, and I'll discuss how a group of co-op students started the first Co-op Community of Practice at Guelph.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.063
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.206
Teacher spread0.200 · 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