MétaCan
Menu
Back to cohort
Record W1852276051 · doi:10.24908/pceea.v0i0.4665

PROCESS OR PRODUCT? BUILDING STUDENT ENGINEERS’ INFORMATION LITERACY SKILLS

2012· article· en· W1852276051 on OpenAlexaffvenue
Norma Godavari, Betty Braaksma

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInformation literacyAccreditationProcess (computing)Product (mathematics)Class (philosophy)LiteracyGRASPComputer scienceEngineering ethicsMathematics educationEngineeringPsychologyPedagogyWorld Wide WebMedical educationSoftware engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Information literacy, defined as the ability to effectively find, use and evaluate information in any medium, is embedded in many, if not most of the CEAB accreditation standards but it remains largely unknown in engineering. Traditional evaluation persists: the research product (such as papers, projects, reports) is rewarded, while the research process is barely acknowledged or ignored completely. The ubiquitous availability of online information has contributed to this perception. In spite of mounting evidence to the contrary, the myth of the “digital native” still lulls educators into believing that all students are expert searchers who have an intuitive grasp of the academic research process. We propose to show how information literacy, which includes visual and digital literacies, is essential to engineering students’ success. We will present the basic fundamentals of information literacy within a professional school such as engineering. We will also discuss recent changes and its impacts on information literacy in an engineering technical communications class.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.005
GPT teacher head0.264
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicLibrary Science and Information LiteracyFrench-language works237,207