MétaCan
Menu
Back to cohort
Record W7008785113

Cyber Discovery evaluation : published 13 August 2021

2021· other· en· W7008785113 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Education Resource Archive (University College London) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Quarter (Canadian coin)Ethnic groupEliteCareer PathwaysSet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

Policy contextThe government's National Cyber Security Strategy 2016 set aside £1.9 billion to drive forward the UK cyber security agenda and ensure that the UK is secure and resilient to cyber threats Among other initiatives, it recognised the importance of identifying and training young people to enter the cyber security profession and pledged to develop a "selfstanding skills strategy that builds on existing work to integrate cyber security into the education system" Participation 23,636 students registered for Cyber Discovery in Year One, growing to 27,903 in Year Two and 35,941 students in Year Three.This dropped slightly to 28,232 in Year Four, perhaps due to the impact of school closures during the COVID-19 pandemicThe proportion of girls registered for Assess reached a third in Years Three (32%) and Four (33%), compared to a quarter (25%) in Year Two and about a fifth (21%) in Year One.The increase in female participants is largely due to the programme being expanded to 13-year-olds, where a higher rate of female participants was driven by targeted marketing campaigns Cyber Discovery engaged a greater proportion of ethnic minority participants than sit computer science school exams Participation tended to be highest in the less deprived areas and in the South of England EngagementThose taking part tended to do so as they felt it would be enjoyable and useful.Improving skills was important, with both qualitative and quantitative evidence showing that career consideration was not a major motivating factor In the first three years, 590 students took part in the Elite phase and completed SANS professional level 6-day training courses in Years Two and Three.89 Elite participants achieved GIAC certification in Year Two (93% pass rate) and 124 in Year Three (89% pass rate) Participants reported that their experiences of the programme were strengthened by being part of a group, having access to support and guidance, and the extension of the programme during the pandemic.Demands on students' time, particularly for those sitting exams, were a barrier.During school closures, a minor theme from qualitative research was that access to a suitable computer and internet connection was an additional barrier Club Leaders and students found the platform engaging, user-friendly and intuitive.They found the level of challenge appropriate and often welcomed being pushed.A minor theme was that it was sometimes too challenging if students did not have the required soft skills and interest in cyber security Cyber careersStudents generally had a very positive view of cyber careers, with most agreeing that they were important for society (94%), suitable for someone like them (80%), and open to anyone regardless of background (66%) Students were more likely to get information on cyber security careers from Cyber Discovery than from other sources, and after taking part most felt they understood the requirements for pursuing a cyber career Survey data suggested that the programme increased skills in computer science and cyber security.There was no evidence that it increased interest in cyber security as a study subject or as a career -this may be due to study methodology or increased knowledge leading some participants to realise that they did not wish to pursue cyber further.Equally, it may reflect the initial objective of the programme which was to engage an elite cohort of most talented young people, rather than appeal to a wider audience Additional outcomesFor industry experts, outcomes centred around the potential for recruitment, including the opportunity to meet talented students and raise awareness of cyber security roles Club Leaders and industry experts generally felt it was possible that the programme could contribute towards longer-term impact and closing the cyber security skills gap Club Leaders were split as to whether Cyber Discovery needed to be linked to the curriculum.A common theme was that content enhanced the curriculum and met the needs of students seeking to broaden their knowledge and skills

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)0.019
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other
Teacher disagreement score0.608
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0120.005
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.6080.299

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDigital Education Resource Archive (University College London)French-language works237,207