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Record W4366758570 · doi:10.1080/10572317.2023.2198877

Bursaries Reimagined: Addressing Digital Inequity through a Library-Led, University-Wide Laptop Bursary Program

2023· article· en· W4366758570 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.

Bibliographic record

VenueThe International Information & Library Review · 2023
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsMacEwan University
Fundersnot available
KeywordsBursaryLaptopScope (computer science)Higher educationLibrary scienceSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The rapid switch to online learning in early 2020 exacerbated problems students were already having with obtaining and maintaining up-to-date devices and a reliable internet connection. MacEwan University Library began offering 4-month term laptop loans at the beginning of the pandemic, but it was clear this was not fully meeting student needs. In response to conversations with faculty and students, the library secured funds from the university’s Student Technology Fee to launch a laptop bursary pilot in Winter 2022, which in turn expanded to a university-wide bursary in Fall 2022. This article discusses why an in-kind laptop bursary was the right approach at the right time in this setting; how this initiative contributes to equity and accessibility; and finally, perceptions of the value of this work, its fit within the scope of the library, and how the unique position of the library as a student-focused service and academic unit positioned it well to successfully offer this bursary. Challenges and opportunities for improvement are also discussed.

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, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.508
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.042
Open science0.0040.003
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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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