CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Deliverables
The English version of the website will be online by M2 of the project
Description of Work and Role of Specific BeneficiariesTask 21 Employee task surveys Lead partner UL Timeline M8 M18 Total number of Person Months allocated to secondments 14In this Task 21 a scientific survey will be designed in order to measure the suitability for machine learning on each task and required skills of employees of participating organizations Strategies in the design of survey will be evaluated in terms of project objectives in line with the fit for use concept This evaluation will be done prior to the actual data collection to choose the optimal sampling strategy UOW KHAS and UPM will design the scientific surveyTask 22 Implementing the model with survey dataLead partner ARC Timeline M18 M32 Total number of Person Months allocated to secondments 12In this Task 22 the collected data will be evaluated with the proposed machine learning model in order to predict the occupation ARC JONL and ICBE will execute the survey ITCL will carry out the preprocessing operation on survey results and execute the fine tuning as necessary KHAS will implement the machine learning model with the tuned data in order to present the occupation categories either tobeextinct or tobesurvived as well as the related skills per categoryTask 23 Gender analysis of survey data and model Lead partner UOW Timeline M18 M30 Total number of Person Months allocated to secondments 12The output of the developed model will be compared in terms of the gender dimension in Task 23 in order to detect the current and nearfuture occupational risks UOW UPM and UL will analyse the survey datamodel output in comparison with the Strategic HRM approach The model will be tested for gender bias using the survey data It will be checked to see if its predictions are more accurate for one gender or gives results that are skewed towards one gender When any such unintentional algorithmic biases are identified remedial strategies will be developed Task 24 Design TrainingEducation sets based on survey Lead partnerUPM Timeline M24 M42 Total number of Person Months allocated to secondments 12According to the survey and model outcomes required training sets will be developed by KHAS UL UPM and UOW would work on training design for management occupation In addition innovative learning platform will be designed To develop a framework of 21st century skills to be promoted we differentiate between declarative know what and procedural knowledge know how and understand skills as domain related declarative knowledge and procedural knowledge of how why and when to apply the procedural knowledge to answer questions and solve problems
Dissemination and Communication plan (opens in new window)This deliverable will detail the communication plan and the strategy for disseminating the findings of the project the different dissemination channels and the target audiences
Report on accuracy evaluation of the ML model (opens in new window)Description of Work and Role of Specific Beneficiaries:Task 1.1: Creation of Data Dictionary and Combined machine learning approaches for task and skill-based modelling of occupationLead partner: ITCL; Timeline: M1 – M16; Total number of Person Months allocated to secondments: 20.To create a synergy between the partners and the work packages a detailed data dictionary will be composed as the first study of Task1.1. Then, some classifiers (such as Linear Discriminant Analysis, Support Vector Machine, Decision Tree, Random Forest) will be implemented with several feature selection methodologies (such as ReliefF, ILFS, HDMR, Laplacian etc.) to determine a highly accurate model for the classification of the skills and tasks either they are routine or non-routine. KHAS will be involved to the machine learning methodology development through the secondments of its ER and ESRs. ITCL will execute the pre and postprocessing of the OECD and O*SET data sets through the secondments of its technical staff. In order to assist KHAS and ITCL, ARC, ICBE and JONL will analyse the existing data sets in terms of real human information depending on their Human Resources (HR) departments.Task 1.2: Building a comprehensive deep learning model with automation probabilityLead partner: KHAS; Timeline: M1 – M20; Total number of Person Months allocated to secondments: 16.In this Task1.2, in accordance with the activities of Task1.1, deep-learning methodologies will be combined with the previous model in order to predict the tasks within the occupation models, which might disappear, continue or transform in near future. ITCL will apply different deep learning methodologies with the previous model developed in Task1.1 in order to determine the automation probability of occupations. KHAS will transform and improve the automation probability model of occupations in order to predict the occupation models, which might disappear, continue or transform in near future.Task 1.3: Testing the model Lead partner: ARC; Timeline: M10 – M36; Total number of Person Months allocated to secondments: 16.Pilot data from real employees of ARC, ICBE and JONL will be collected with a short survey which is in OECD dataset format. The results of survey will be pre-processed and used for the verification of the classifier. ARC and ITCL will develop and execute a survey in order to collect pilot data for the verification of the classifier developed in Task1.1 applied into the two different contexts (i.e. two different sectors and two different countries/cultures). ITCL will execute the pre-processing of the collected data sets through the secondments of its technical staff. KHAS will use the pre-processed survey for the verification of the classifier through the secondments of its ER and ESRs.
Description of Work and Role of Specific Beneficiaries:Task 2.1: Employee & task surveys Lead partner: UL; Timeline: M8 – M18; Total number of Person Months allocated to secondments: 14.In this Task 2.1, a scientific survey will be designed in order to measure the suitability for machine learning on each task and required skills of employees of participating organizations. Strategies in the design of survey will be evaluated in terms of project objectives, in line with the ‘fit for use’ concept. This evaluation will be done prior to the actual data collection to choose the optimal sampling strategy. UOW, KHAS and UPM will design the scientific survey.Task 2.2: Implementing the model with survey dataLead partner: ARC; Timeline: M18 – M32; Total number of Person Months allocated to secondments: 12.In this Task 2.2, the collected data will be evaluated with the proposed machine learning model in order to predict the occupation. ARC, JONL and ICBE will execute the survey. ITCL will carry out the pre-processing operation on survey results and execute the ‘fine tuning’ as necessary. KHAS will implement the machine learning model with the tuned data in order to present the occupation categories either to-be-extinct or to-be-survived as well as the related skills per category.Task 2.3: Gender analysis of survey data and model Lead partner: UOW; Timeline: M18 – M30; Total number of Person Months allocated to secondments: 12.The output of the developed model will be compared in terms of the gender dimension in Task 2.3 in order to detect the current and near-future occupational risks. UOW, UPM and UL will analyse the survey data/model output in comparison with the Strategic HRM approach. The model will be tested for gender bias using the survey data. It will be checked to see if its predictions are more accurate for one gender or gives results that are skewed towards one gender. When any such unintentional algorithmic biases are identified, remedial strategies will be developed. Task 2.4: Design Training/Education sets based on survey Lead partner:UPM; Timeline: M24 – M42; Total number of Person Months allocated to secondments: 12.According to the survey and model outcomes, required training sets will be developed by KHAS, UL, UPM and UOW would work on training design for management occupation. In addition, innovative learning platform will be designed. To develop a framework of 21st century skills to be promoted, we differentiate between declarative (know what) and procedural knowledge (know how) and understand skills as domain related declarative knowledge and procedural knowledge of how, why, and when to apply the procedural knowledge to answer questions and solve problems.
Publications
Author(s):
Iago Xabier Vázquez García, Damla Partanaz, Emrullah Fatih Yetkin
Published in:
arXivi, Issue 10 Jul 2025, 2025, Page(s) 1-27
Publisher:
arXivi
DOI:
10.48550/arxiv.2507.07582
Author(s):
María Navas-Loro1 , Julián Arenas-Guerrero1 and Elena Montiel-Ponsoda
Published in:
CEUR Workshop Proceedings, Issue Montly 12 per, 2023, Page(s) 5-10, ISSN 1613-0073
Publisher:
CEUR Workshop Proceedings
DOI:
10.5281/zenodo.10082602
Author(s):
Navarro-López, Eva M.
Godwin, Eun S.
Yamak, Sibel
Mahmood, Samia
Thelwall, Mike
Ucal, Meltem
Rahimi, Roya
Published in:
Institute for Gender and Diversity in Organizations Working Paper Series, Issue 2023, 2023, Page(s) 12-21
Publisher:
Institute Gender and Diversity in Organizations
DOI:
10.57938/wp15.2023.005
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