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Record W6969819598 · doi:10.5683/sp3/4pxewi

Supporting data for the University of Ottawa Undergraduate Students Textbook Affordability Survey/Données connexes de l’Enquête sur l’accessibilité des manuels scolaires – Université d’Ottawa, 1er cycle

2024· dataset· fr· W6969819598 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languagefr
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMicrosoft excelDescriptive statisticsPopulationResearch methodologyAccess to information

Abstract

fetched live from OpenAlex

La Bibliothèque de l'Université d'Ottawa a mené une enquête sur l'accessibilité des manuels scolaires auprès des étudiantes et étudiants de premier cycle à l'Université d'Ottawa afin de recueillir des renseignements sur les dépenses liées aux manuels et leur impact sur la population étudiante. Au total, 1 687 personnes ont répondu à des questions sur les montants dépensés, les décisions d'achat et les aternatives aux manuels. Le sondage a été administré exclusivement aux étudiantes et étudiants de premier cycle en février 2023 par l'intermédiaire du Syndicat étudiant de l'Université d'Ottawa (SÉUO) à l'aide de Microsoft Forms. Les résultats ont été téléchargés sous forme d'un tableau Excel et les réponses aux trois questions ouvertes ont été codées. Des statistiques descriptives ont été générées à l'aide d'Excel. L'ensemble de données contient les données originales avec le codage, une copie de conversation, les questions d'enquête, les catégories de codage pour les trois questions ouvertes et ce fichier README. The University of Ottawa Library conducted the "University of Ottawa Undergraduate Students Textbook Affordability Survey" to gather information on textbook expenses and their impact on students. A total of 1,687 respondents answered questions about amounts spent, decisions around purchases, and alternatives to textbooks. The survey was administered exclusively to undergraduate students in February 2023 through the University of Ottawa Students' Union (UOSU) using Microsoft Forms. Results were downloaded as an Excel spreadsheet and answers to three open questions were coded. Descriptive statistics were generated using Excel. The dataset contains the original data with coding, a preservation copy, survey questions, coding categories for three open questions, and this README file.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.447
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.015
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4470.072

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 head0.114
GPT teacher head0.365
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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