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Record W2466535610 · doi:10.1145/2899415.2899420

Global Perspectives on the Role of Two-Year/Technical/Junior Colleges in Computing Education

2016· article· en· W2466535610 on OpenAlexaff
Cara Tang, Elizabeth K. Hawthorne, Cindy Tucker, Ernesto Cuadros-Vargas, Diana Cukierman, Ming Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInformation Systems Education and Curriculum Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBachelorVariety (cybernetics)Mathematics educationCareer PathwaysComputer scienceMedical educationPolitical sciencePsychologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

This panel presents varying global perspectives on the role of community colleges and 2- or 3-year technical schools (collectively called junior colleges here) in computing education. In some countries, students interested in a career in computing can obtain a 2- or 3-year degree instead of, or as a precursor to, a traditional Bachelor's degree. With representatives from five different countries and four different continents, the panel discusses the variety of pathways in computing education around the world, and in particular the role of community colleges and 2- or 3-year technical schools in these pathways.

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)0.013
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.004
Scholarly communication0.0100.005
Open science0.0010.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.263
Teacher spread0.256 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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