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"fi": "Aineistossa on tarkasteltu eri kansalaisryhmien yhteiskunnallisia ja poliittisia mielipiteitä. Vastaajat ottivat kantaa väitteisiin, jotka käsittelivät yhteiskunnallista osallistumista ja vaikuttamista, luottamusta poliittisiin päättäjiin, vastaajan poliittista kiinnostusta sekä poliittisia mielipiteitä. Aineisto kerättiin osana Kansalaisuuden kuilut ja kuplat (BIBU) -hanketta, joka tutkii, miten talouden rakennemuutokset haastavat poliittista päätöksentekoa. BIBU selvittää kansalaisten ja päätöksentekijöiden toimintakykyä sekä poliittisia intressejä ja tunteita. Hankkeessa on mukana kuusi eri yliopistoa ja tutkimuslaitosta, ja sitä vetää professori Anu Kantola Helsingin yliopistosta. Vastaajia pyydettiin aluksi arvioimaan väittämiä, jotka käsittelivät erilaisia yhteiskunnallisen osallistumisen muotoja, kuten esimerkiksi poliittiseen keskusteluun osallistumista, tuotteiden ja palveluiden kuluttamista poliittisista syistä sekä kansalaistoimintaan osallistumista. Kysyttiin myös luottamuksesta päättäjiin sekä yksittäisen ihmisen vaikutusmahdollisuuksista. Seuraavaksi selvitettiin poliittista suuntautumista ja äänestyskäyttäytymistä. Edelleen tiedusteltiin vastaajan tuntemuksia ja ajatuksia tiettyjä kansalaisryhmiä kohtaan, lapsiin ja lastenkasvatukseen liittyen sekä sosiaaliturvasta. Lopussa vastaajia pyydettiin vielä arvioimaan omaa yhteiskunnallista asemaa sekä tyytyväisyyttä ja terveyttä. Taustamuuttujia ovat muun muassa vastaajan sukupuoli, syntymävuosi, siviilisääty, koulutustaso, ammatti, bruttotulot, työmarkkina-asema sekä kotitalouden koko."
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"en": "The survey charted the careers and employment situations of Finnish doctoral graduates three years after they had obtained their degree in 2016. Themes of the survey included impact of a doctoral degree on employment, job satisfaction and development suggestions for doctoral education. The respondents were first asked to what extent different reasons had impacted their decision to take up a doctoral degree, and how they had financed their studies. They were asked to estimate the share of time they had spent on their doctoral dissertation alongside other work. Relating to employment situation and quality of work, questions charted how the degree had affected the respondents' status in the job market and what their occupational status was at the time of the survey and what it had been 6 months before starting the degree and 6 months after finishing it. The respondents were asked if they had been unemployed after finishing their degree, and reasons were charted that potentially had hindered their employment. The respondents who were in paid work were asked the type of their current work tasks and the competences needed in their work. It was also examined whether a doctoral degree was a prerequisite for their current job, how well the respondents' work corresponded to their qualifications, and whether their work was related to the topic of their doctoral dissertation. Gross monthly income was also charted. With regard to the significance of a doctoral degree in occupational life, general satisfaction with the degree was charted along with opinions on whether doctoral education was useful when it came to different types of competences needed in occupational life. Relating to developing and improving doctoral education, views were probed on things that should be emphasised in the education (e.g. skills related to teaching, entrepreneurship, management, popularisation of research results, career planning, presentation skills). Background variables included the respondent's gender, field of study, and age at the time of graduation (dichotomised \"under 36\"/\"36 and over\").",
"fi": "Uraseurannoilla kerätään vuosittain valtakunnallisesti tietoa yliopistoista valmistuneiden työllistymisestä ja työelämän osaamistarpeista. Vuoden 2019 tohtoriuraseurannalla kartoitettiin vuonna 2016 suomalaisista yliopistoista valmistuneiden tohtoreiden varhaista työuraa ja työtilannetta kolme vuotta valmistumisen jälkeen. Kyselyn teemoja ovat muun muassa tohtorikoulutuksen vaikutus työllistymiseen, koulutuksen vastaavuus työelämän osaamistarpeisiin nähden ja nykyisen työsuhteen laatu. Ensiksi vastaajilta kysyttiin heidän työmarkkinatilanteestaan tohtorin tutkinnon suorittamista ennen ja sen jälkeen sekä mahdollisista työttömyysjaksoista tutkinnon suorittamisen jälkeen. Seuraavaksi kysyttiin vastaajan nykyisestä työsuhteesta ja sen laadusta sekä tiedusteltiin tohtorikoulutuksen ja tutkinnon merkitystä. Vastaajan tuli muun muassa arvioida, kuinka tohtorikoulutus kehitti erilaisia työelämävalmiuksia ja kuinka tärkeitä nämä tiedot ja taidot ovat vastaajan nykyisessä työssä. Lopuksi vastaajilta kysyttiin tohtoriopintojen rahoituksesta ja ensisijaisesta suoritustavasta. Taustamuuttujina aineistossa ovat vastaajan sukupuoli, koulutusala ja ikä valmistuessa kaksiluokkaisena."
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"fi": "<jats:p>Abstract. Wildfires present severe threats to various aspects of ecosystems, human settlements, and the environment. Early detection plays a critical role in minimizing the destructive consequences of wildfires. This study introduces an innovative approach for smoke-based wildfire detection in Boreal forests by combining the YOLO V5 algorithm and transfer learning. YOLO V5 is renowned for its real-time performance and accuracy in object detection. Given the scarcity of labelled smoke images specific to wildfire scenes, transfer learning techniques are employed to address this limitation. Initially, the generalisability of smoke as an object is examined by utilising wildfire data collected from diverse environments for fine-tuning and testing purposes in Boreal forest scenarios. Subsequently, Boreal forest fire data is employed for training and fine-tuning to achieve high detection accuracy and explore benchmarks for effective local training data. This approach minimises extensive manual labelling efforts while enhancing the accuracy of smoke-based wildfire detection in Boreal forest environments. Experimental results validate the efficacy of the proposed approach. The combined YOLO V5 and transfer learning framework demonstrates a high detection accuracy, making it a promising solution for automated wildfire detection systems. Implementing this methodology can potentially enhance early detection and response to wildfires in Boreal forest regions, thereby contributing to improved disaster management and mitigation strategies.\n </jats:p>",
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