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Record W2473766665 · doi:10.12753/2066-026x-14-033

MASSIVE OPEN ONLINE COURSES AS E-BRICKS FOR SMART CITIES

2014· article· en· W2473766665 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueeLearning and Software for Education · 2014
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesPopulationSmart cityMassive open online courseOpen educationComputer scienceKnowledge managementBusinessWorld Wide WebSociologyInternet of Things

Abstract

fetched live from OpenAlex

Open Educational Resources and Massive Open Online Courses as e-bricks for Smart Cities Authors: Carmen Holotescu, Gabriela Grosseck, Laura Malita Nowadays when more than half of the world's population lives in urban areas, when information and (mobile) communication technologies are real catalysts for innovations in all domains, there are a lot of studies and debates related to how our cities should become "Smart Cities", "Smarter Cities" or "Future Cities", in order to improve life quality and to reduce costs. The paper starts with a literature research related to definitions for the "Smart City" term and to the needed steps / action plans / strategies for such a transformation. The new citizens will have vital roles in building smart cities; they should be hyperconnected, creative, entrepreneurs, also they should actively participate and collaborate in the cities activities and decisions. The paper will explore: - How Open Educational Resources and Massive Open Online Courses can support the citizens engagement, learning and participation, also new skills and competencies development? - How the authorities can collaborate with universities and researchers to develop specific OER and to organize such courses? Which new policies are neeeded? - Which features should be offered by MOOCs platforms and how such courses can be facilitated? - What lessons can be learned from current projects targeting these issues? References: Bacsich, P., & Pepler, G. (2013). Learner Use of Online Content.Teaching and Learning Online: New Models of Learning for a Connected World, 2, 75. Buchem, I., & P?rez-Sanagust?n, M. (2013). Personal Learning Environments in Smart Cities: Current Approaches and Future Scenarios. http://openeducationeuropa.eu/sites/default/files/asset/In-depth_35_1.pdf Department for Business, Innovation and Skills, London. (2013). The Maturing of the MOOC. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/240193/13-1173-maturing-of-the-mooc.pdf Falconer, I., McGill, L., Littlejohn, A., Boursinou, E., & Punie, Y. (2013). Overview and Analysis of Practices with Open Educational Resources in Adult Education in Europe. ftp://ftp.jrc.es/pub/EURdoc/JRC85471.pdf

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.324
Teacher spread0.308 · 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