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Record W2991708869

Issues in Forming School District Consortia to Provide Distance Education: Lessons from Alberta

2007· article· en· W2991708869 on OpenAlexaffvenueabout
Margaret Haughey, Tara Fenwick

Bibliographic record

VenueInternational journal of e-learning & distance education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Policies
Canadian institutionsSt. Francis Xavier UniversityAthabasca University
Fundersnot available
KeywordsPolitical scienceDecentralizationConceptualizationHumanitiesPublic administrationLibrary scienceArtComputer science
DOInot available

Abstract

fetched live from OpenAlex

onsortia are a popular way for organizations to extend their activities without incurring the costs involved to expand their own operations. School system consortia developed in Alberta in response to the Alberta Government's decentralization of distance education in 1989. Jurisdictions formed consortia to co-operate, to pool resources, and to be cost-effective. The five consortia provide many examples of the issues faced in developing and operating of consortia, and from these eight issues, implications are drawn. Finally, Kanter's conceptualization provides a framework for identifying varieties of consortia according to intent and interdependence. Les organisations ont volontiers recours aux consortiums afin d'etendre leurs activites, puisque ce moyen leur permet d'eviter les depenses habituellement liees a l'expansion de leurs operations. En Alberta, des circonscriptions scolaires ont donc mis sur pied des consortiums a la suite des mesures de decentralisation de l'education a distance entreprises par le gouvernement de cette province en 1989. Les consortiums ont ainsi ete constitues par ces circonscriptions dans le but de collaborer, de partager leurs ressources et de reduire leurs couts. L'article decrit ces cinq consortiums, brosse un tableau des problemes survenus au cours de leur mise sur pied et de leur fonctionnement, et analyse notamment huit problemes ainsi que leurs repercussions. Finalement, la conceptualisation faite par Kanter offre une structure d'identification des divers consortiums possibles selon les objectifs vises et leur interdependance.

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)0.016
metaresearch head score (Gemma)0.019
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 score0.075
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0190.005
Scholarly communication0.0090.004
Open science0.0050.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.413
Teacher spread0.392 · 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

Citations11
Published2007
Admission routes3
Has abstractyes

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