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Record W1576791349 · doi:10.21432/t25p5b

Open Source Software and Schools: New Opportunities and Directions

2005· article· en· W1576791349 on OpenAlexvenueno aff
Gary Hepburn

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2005
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwareSoftware peer reviewFlexibility (engineering)Social software engineeringComputer scienceSoftware developmentInformation and Communications TechnologySoftware engineeringSoftware constructionKnowledge managementWorld Wide WebOperating systemManagementEconomics

Abstract

fetched live from OpenAlex

Abstract: Integrating information and communication technology into schools has been challenging. A central component of the challenge is coping with the expense and usage restrictions of software that is installed on school computers. An alternative approach to the educational software problem is, however, emerging. This approach involves making greater use of open source software. In many cases open source software can effectively replace the proprietary or commercial software that dominates the educational computing landscape. Using this software option would result in decreased costs, increased flexibility, and increased opportunities to address social and ethical issues related to information and communication technology. In order to responsibly spend taxpayers’ money and to maximize the potential of information and communication technology in education, it is important that educators learn about open source software and challenge conceptions that give priority to proprietary software.

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)0.020
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0040.016
Scholarly communication0.0140.031
Open science0.0020.006
Research integrity0.0120.008
Insufficient payload (model declined to judge)0.0170.002

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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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 designNot applicable
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

Citations8
Published2005
Admission routes1
Has abstractyes

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