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Record W4313051211 · doi:10.1079/cabicomm-62-8170

Evaluation of MAS-ICM 2015-2020

2022· report· en· W4313051211 on OpenAlexfundno aff
Hariet Hinz, Manfred Grossrieder, Christine Jurt, Vicuña Muñoz, Frances Williams

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
FundersAustralian Centre for International Agricultural ResearchAgriculture and Agri-Food CanadaMinistry of Agriculture of the People's Republic of China
KeywordsComputer science

Abstract

fetched live from OpenAlex

The Masters of Advanced Studies in Integrated Crop Management (MAS ICM) was a higher education course which ran from 2015 to 2020. The interdisciplinary course provided knowledge on ICM within a sustainable agricultural production system, and the course was offered to agricultural professionals from developing countries. This assessment aimed to determine the actual benefits experienced by the 66 graduated students, which mainly came from Africa and Asia and 40% of which were women. A questionnaire was developed that mainly focused on quantifying the longer-term impacts of the course after the students had returned home. Participants reported a variety of different impacts, including job promotion, increased confidence and respect in their opinions, as well as increased knowledge and soft skills. The study concluded that knowledge transfer might be as efficient in virtual online courses as face-to-face courses. However, the gain in soft skills, that was a key benefit for MAS ICM students, would be challenging for those participating in a virtual course to obtain. An online Certificate of Advanced Studies course is currently being rolled out by CABI and would profit from a similar evaluation in a few years' time.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.693
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0630.000

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 head0.241
GPT teacher head0.380
Teacher spread0.139 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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