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Record W2593186120 · doi:10.5430/ijhe.v6n2p50

Development of A Study Module on and Pedagogical Approaches to Industrial Environmental Engineering and Sustainability in Mozambique

2017· article· en· W2593186120 on OpenAlexvenueno aff
Roope Husgafvel, Mikko Martikka, Andrade Egas, Natasha Ribeiro

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
FundersUlkoministeriö
KeywordsSustainabilityWork (physics)CurriculumSustainable developmentEngineering educationProcess (computing)Environmental educationEngineering managementEngineeringEngineering ethicsBusinessEnvironmental planningEnvironmental resource managementPolitical scienceSociologyComputer sciencePedagogyGeographyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Addressing the sustainability challenges in the forest sector in Mozambique require capacity building for higher education and training of new skilled expert and future decision-makers. Our approach was to developed a study module on and pedagogical approaches to industrial environmental engineering and sustainability. The idea was to developed a joint module that would eventually become a part of both PhD and MSc programmes in the Eduardo Mondlane University (UEM) in Mozambique. The basis of our development work encompassed the local priorities as identified by the UEM staff, UEM competencies in forestry engineering and the experience of the Aalto University in higher education in the fields of environmental engineering, sustainability and forest products technology. From the beginning, public authorities and industry/company representatives were involved in the development process to advance the created benefits in terms of sustainable development in Mozambique. The result of the joined work by these two higher education institutions was a study module that has been teached and completed by a class of MSc students as a part of the official UEM curricula.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.214

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 communication0.0000.000
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 head0.143
GPT teacher head0.357
Teacher spread0.214 · 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
Published2017
Admission routes1
Has abstractyes

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