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Record W7061816198

Shared Services Study Report 2 - The software currently in use for administrative systems in UK FE and HE

2012· other· en· W7061816198 on OpenAlexaboutno aff

Bibliographic record

VenueJISC Information Environment Repository (Jisc) · 2012
Typeother
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDominance (genetics)Higher educationProduct (mathematics)Market shareQuarter (Canadian coin)Blackboard (design pattern)Variety (cybernetics)
DOInot available

Abstract

fetched live from OpenAlex

1. The systems market for further and higher education is very fragmented. There are a large number of different suppliers across the range of systems we looked at. In some individual areas there are a considerable number of different suppliers. There is also remarkably little overlap of the products used between the two sectors. 
\n2. The nine functional areas we consider in this report show a variety of patterns of use and market share. 
\n3. In each of finance, student records and timetabling, the higher education market has one player significantly larger than any of the others and has a relatively small number of other players. The dominant player in each equivalent FE market has a smaller share than in HE and there are more of the smaller players. 
\n4. CRM is the area which shows the least maturity. More than 40% of FEIs responding indicated that they made no use of a CRM and about half of HEIs responded the same way. 
\n5. A large number of estates systems are in use and they typically have a low level of integration with other corporate systems. Only one product had more than a 10% share of either the FE or the HE market. A quarter of those responding from higher education said that no estates system is in use at their institution. 
\n6. The area of VLEs shows complete dominance of the market by Moodle and Blackboard. In further education, Moodle has just over half the market with Blackboard having a further 30%. In higher education, Blackboard

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.226
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes1
Has abstractyes

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