Prior research has been limited in examining emotional intimate partner violence (EIPV) related attitudes and cognitions. To address these research gaps, the current study aimed to build a better understanding of EIPV recognition and myth acceptance. Firstly, we developed and investigated reliability and factor structure, including factorial invariance for sex groups, of the Emotional Intimate Partner Violence Recognition (EIPV-R) Scale and the Emotional Intimate Partner Violence Myth Acceptance (EIPV-MA) Scale. Secondly, we explored associations between EIPV-R and EIPV-MA scores and external criteria. Study participants were a representative sample of young adults (aged 18-29 years) in Poland (N = 2705; 53.4% female). EIPV-R and EIPV-MA demonstrated strong psychometric properties and factorial invariance across sex. Both instruments were also found to be multidimensional, with five factors best capturing the EIPV-R scores (emotional withholding, controlling behavior, dominance and intimidation, severe threatening behavior, denigration) and three factors best capturing the EIPV-MA scores (romanticization of abuse, victim blaming, constrained perception of emotional abuse). Further analyses revealed than men are more likely to endorse rape myths and have more difficulties recognizing EIPV than women. Individuals who experienced intimate partner violence (IPV positive) scored significantly lower than IPV negative participants on total EIPV-R as well as on three subscales (controlling behavior, dominance and intimidation, denigration). However, IPV positive individuals scored higher on the severe threatening behavior subscale, which may be because the ability to identify overtly dangerous acts is vital for safety and survival. The current research findings can be used to build effective interventions aimed at reducing the prevalence of EIPV.
Timed security protocols rely on freshness tokens whose validity must hold when a response is delivered, not merely when it is sent. Many models collapse sending and delivery into one event, erasing the transit interval in which a token may expire. This abstraction has a concrete consequence: a delay-oblivious model can report that successful completion is reachable for a protocol that, on any network with a positive minimum transit latency, can never complete – a soundness gap rather than a missed attack. We introduce Split-Step Timed Interpreted Systems (SSTIS), a dense-time semantics in which every step has three phases – send, transit, and deliver-or-fail – making the transit interval and a delivery failure explicit local states.We formalise interpretations into classical Timed Interpreted Systems (TIS) and into SSTIS, and prove three results: SSTIS conservatively extends TIS at zero delay; a delivery-time failure is reachable in SSTIS but unsatisfiable in TIS by construction; and under a minimum transit delay D, a step whose freshness window L satisfies D > L fails in every run, including the fastest adversary-free delivery, so the protocol is unrealizable. From this we derive a linear-time realizability decision procedure that decides whether a guarded step can ever succeed, as a static pre-check before bounded model checking. We give a quantifier-free linear-real-arithmetic encoding with soundness and completeness proofs, apply it to the MobInfoSec SP2SP_MutualAuth protocol and classical timestamp families, and report a feasibility study across 779 protocol-variant instances and 12,410 reachability queries solved in under a second at the median.
Automated Machine Learning (AutoML) reduces the technical requirements for applying machine learning, yet existing systems still demand technical expertise, optimize inefficiently, and rarely support conversational use. We present a conversational AutoML system with a five-agent architecture coordinated via natural language, tuning Random Forest and gradient-boosting models (XGBoost, LightGBM, and CatBoost) with Monte Carlo Tree Search (MCTS) and Bayesian optimization under repeated cross-validation conditions. Its distinctive component is an ensemble coordinator: specialist optimizer agents tune model families concurrently, and a coordinator fuses their outputs with the strongest baseline into a stacking ensemble, deploying it only when it improves on the best single model by cross-validation. This separates two measurable benefits: a speed-up from parallel search and an accuracy gain from ensemble fusion. On six benchmark datasets (five classification and one regression), the multi-agent coordinator is best or tied on every dataset and the only configuration with a positive average gain over defaults (+0.20 pp, versus −0.69 pp for single-agent MCTS); its search is 2.3× faster than the single-agent Bayesian baseline in aggregate, and it is systematically better than single-agent MCTS across datasets (Wilcoxon signed-rank, p = 0.031). While individual dataset gains are observed, none reach statistical significance when adjusted for multiple comparisons. For classification tasks, we additionally report F1-score, precision, recall, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve).
Political trust is essential for implementing the United Nations 2030 Agenda, particularly Sustainable Development Goal (SDG) 16 on building effective, accountable and inclusive institutions. At the same time, there has been a long-standing decline in political trust within democratic countries, which presents a considerable obstacle to the enactment of sustainable development policies. Although prior research has explored the relationship between economic conditions and political trust, evidence on how different dimensions of inequality jointly shape trust remains limited. This study addresses this gap by analysing how economic inequality, regional economic disparities, and subjective income perceptions affect political trust. Using data from the European Social Survey (Round 9), we estimate multilevel models that account for both individual- and country-level factors. The results demonstrate a negative relationship between individual income and political trust, while lower economic inequality strengthens this negative relationship. Our findings highlight that reducing economic inequality is crucial for enhancing political trust, suggesting that governments should prioritize equitable resource distribution and address regional disparities to foster trust in institutions. By integrating subjective well-being with objective economic indicators, this research offers a comprehensive view of how inequality affects political trust across the EU countries and outlines institutional and distributive conditions that support progress toward the SDGs.
ULTRAGEN 2023 - informacje o zbiorze danych
Nazwa projektu: ULTRAGEN. Wchodzenie w dorosłość w czasach ultra-niepewności: międzypokoleniowa teoria „rozedrganych” tranzycji
Numer grantu NCN: 2020/37/B/HS6/01685
Kierowniczka projektu: dr hab. Paula Pustułka, Profesor USWPS
Instytucja realizująca projekt: Uniwersytet SWPS
Wersja 1.0 • 13 października 2026
1 Opis zbioru
1.1 Pochodzenie i wykorzystanie zbioru
Sondaż stanowi komponent projektu ULTRAGEN i został zrealizowany we współpracy z IPSOS w dniach 13-23 stycznia 2023 r. Sondaż objął 2104 osoby w wieku 18-35 lat w Polsce. Próba jest reprezentatywna pod względem wieku, płci, wielkości miejsca zamieszkania i regionu. Różnice względem struktury populacji GUS z 2021 r. wyrównano wagami poststratyfikacyjnymi (waga, zakres 0,481-1,898). Zbiór może być wykorzystywany do analiz sytuacji młodych dorosłych, relacji rodzinnych, pracy, edukacji, mieszkalnictwa, dorosłości, wartości i postaw polityczno-społecznych.
1.2 Rodzaje, formaty i wielkość danych
Plik SAV zawiera 2104 rekordy i 342 zmienne: 328 numerycznych i 14 zmiennych tekstowych. Obejmuje odpowiedzi, metryczkę, identyfikator id i wagę (waga). Zbiór ma 1.15 MB. Dane w zbiorze są zanonimizowane i nie umożliwiają identyfikacji indywidualnych respondentów.
Zbiorowi towarzyszy kwestionariusz badania w formacie PDF. Pełne brzmienie pytań należy uzgadniać z kwestionariuszem, ponieważ część etykiet w pliku SAV jest skrócona.
Metadane repozytorium obejmują dane autorek wraz z numerem ORCID, projekt i numer grantu, datę, wersję, warunki dostępu i licencję.
2 Udostępnianie i licencja
2.1 Zakres i termin dostępu
Zbiór udostępniony zostaje na licencji CC BY-NC 4.0.
2.1 Autorki
Agnieszka Kwiatkowska (ORCID 0000-0002-2241-0831)
Paula Pustułka (ORCID 0000-0001-6679-0365)
Justyna Sarnowska-Wilczyńska (ORCID 0000-0001-5376-7625)
Justyna Kajta (ORCID 0000-0002-2428-8876)
Jowita Radzińska (ORCID 0000-0002-6187-636X)
Agnieszka Golińska (ORCID 0000-0002-0284-7315)
2.2 Identyfikacja i cytowanie zbioru
Kwiatkowska, A., Pustułka, P. Sarnowska-Wilczyńska, J., Kajta, J., Radzińska, J., & Golińska, A. (2023). Sondaż ULTRAGEN 2023 [zbiór danych]. DOI: [DOI]. Licencja: CC BY-NC 4.0.
2.3 Materiały źródłowe
Ultragen2023.sav
Ultragen_kwestionariusz_2023.pdf
