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Record W1919261797 · doi:10.21432/t28k57

Shadow netWorkspace: An open source intranet for learning communities

2006· article· en· W1919261797 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2006
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsIntranetLicenseComputer scienceSoftware deploymentShadow (psychology)World Wide WebOpen source softwareSoftwareOpen sourceWorkspaceKey (lock)Knowledge managementMultimediaSoftware engineeringThe InternetComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Shadow netWorkspace (SNS) is a web application system that allows a school or any type of community to establish an intranet with network workspaces for all members and groups. The goal of SNS has been to make it easy for schools and other educational organizations to provide network services in support of implementing a learning community. SNS is open source software using the GNU General Public License (GPL). The software is freely available, and can be downloaded and distributed under the terms of the GPL. SNS is an ongoing project and this instructional development report describes the system, some ways that it is being used, and some key lessons learned from the development and initial deployment of SNS.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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