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The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

Involvement of lenders for PPP highway projects in India starts after the bid award. The post-award development phase of Toll and Annuity PPPs differ significantly in terms of potential risk assumed by lenders. This study aims to assess the impact of the transparency law on the post-award development phase of Toll and Annuity PPPs.

A unique dataset of 469 PPP highway projects implemented in India was used to conduct this empirical study. An OLS regression model was developed to assess the impact of the transparency law on the post-award development phase.

Enacting the transparency law increased the duration of the post-award development phase of Toll projects; however, its impact on Annuity projects was not significant. Moreover, Toll and Annuity projects with a longer post-award development phase had a shorter construction phase. The post-award development phase of the Toll projects was relatively more sensitive to technical, economic and location-specific variables than Annuity projects. Length of road stretch, duration of the concession period and individual income of end-users significantly impacted the duration of this phase of Toll projects.

Transparency law can improve risk mitigation of Toll projects during the post-award development phase.

The impact of transparency law on PPP projects has never been assessed. This study assesses its impact on the two forms of PPPs. It also highlights the determinants of this phase and how they differ for the two forms of PPPs.

Groups of students were enrolled in a course that sought to produce a three-phase theoretical model over three semesters.

A design project to comprehensively address school violence was launched at a university in eastern Pennsylvania.

This article updates the recent and most critical finding of the project by illuminating specific implications of the importance of teacher training and the development toward competence in recognition of children who are emotionally and psychologically injured through proactive measures such as screening for emotional and psychological well-being.

Although the model has not been tested, screening to identify those in need of emotional support and training to support teachers is clear. Screening and training offer important opportunities to help learners build skills toward resilience to soften the effects of trauma.

A view of the “whole child” with regard to academic success could further foster social and emotional development.

Early intervention can prevent the onset of symptoms associated with posttraumatic stress and related disorders. This effort alone may significantly reduce the uncomfortable incidences and perhaps ultimate prevention of the violence that is perpetuated among children.

Preliminary research supports a continued conversation regarding effective tools to find children emotionally and psychologically at-risk, which allows teachers an opportunity for timely emotional and psychological interventions.

First-generation college students in the United States are a unique demographic that is often characterized by the institutions that serve them with a risk-laden and deficit-based model. However, our analysis of the transcripts of open-ended, semi-structured interviews with 22 “first-gen” respondents suggests they are actively deft, agentic, self-determining parties to processes of identity construction that are both externally imposed and potentially stigmatizing, as well as exemplars of survivance and determination. We deploy a grounded theory approach to an open-coding process, modeled after the extended case method, while viewing our data through a novel synthesis of the dual theoretical lenses of structural and radical/structural symbolic interactionism and intersectional/standpoint feminist traditions, in order to reveal the complex, unfolding, active strategies students used to make sense of their obstacles, successes, co-created identities, and distinctive institutional encounters. We find that contrary to the dictates of prevailing paradigms, identity-building among first-gens is an incremental and bidirectional process through which students actively perceive and engage existing power structures to persist and even thrive amid incredibly trying, challenging, distressing, and even traumatic circumstances. Our findings suggest that successful institutional interventional strategies designed to serve this functionally unique student population (and particularly those tailored to the COVID-moment) would do well to listen deeply to their voices, consider the secondary consequences of “protectionary” policies as potentially more harmful than helpful, and fundamentally, to reexamine the presumption that such students present just institutional risk and vulnerability, but also present a valuable addition to university environments, due to the unique perspective and broader scale of vision their experiences afford them.

Public–Private Partnerships (PPPs) are crucial to the procurement of global infrastructure projects. Moreover, a price mode based on a cluster of core concessionary items is key to the delivery of value-for-money and successful project outcomes. However, existing research has yet to fully identify PPP concessionary items, nor yet described the range of practical price modes. This study provides taxonomy of core concessionary items impacting PPP projects, systematically classifies price modes, and assesses the applicability and risk impacts of those price modes on PPP projects.

This study adopts a comparative case study method in analyzing core concessionary items and alternative price modes. China is taken as the context, as it is one of the world’s largest PPP markets. In ensuring research validity and reliability, diverse data sources are utilized, with a graphic content analysis tool developed to capture the structure of price modes.

Eight PPP price modes are identified. These are: (1) UP (Unit Price) mode, (2) ALS (Annual Lump Sum) mode, (3) IRR (Internal Rate of Return) mode, (4) RP (Return for Investing Capital (RIC) - Profit Rate of O&M (PROM)) mode, (5) RFP (RIC - Financing Interest Rate (FR) - PROM) mode, (6) RFPL (RIC - FR - PROM - Lower Limit of User Charge (LLoUC)) mode, (7) RFL (RIC - FR - Lump Sum/Fixed Unit Price O&M Contract (LSOM/FUP)) mode, and (8) RFLL (RIC - FR - LSOM/FUP - LLoUC) mode. Other main findings are as follows: (1) Five risk allocation configurations can be achieved via these price modes. Yet while different price modes enable the allocation of specific risks, these do not always align with contracting parties’ original intentions. (2) IRR and RP modes may be less applicable in general because of their vulnerability in allocating critical risks and capacity for spurring opportunistic behavior.

By depicting the paths by which concessionary items in price modes affect cash flow, a systematic analysis of price modes was conducted exposing structural characteristics, along with risk allocation choice implications. The study is unique in: (1) Providing a systematic classification of PPP price modes used in PPP projects, (2) Presenting a comprehensive identification and streamlining of concessionary items in PPP practice, and (3) Analyzing the risk effects of different price modes. Together, these outcomes offer a hitherto unavailable perspective on PPP project risk management. The value of the study lies in the following: (1) Existing studies employ diverse concessionary items, but their applicability varies. This study offers an overarching framework facilitating decision-making in selecting appropriate PPP price modes and in determining concessionary items. (2) This study adds to the understanding of PPP price modes in significant ways that will aid local governments and potential sponsors in crafting and administrating more workable contract designs.

The purpose of this study was to investigate a framework for the implementation of freedom of information (FOI) legislation in South Africa, against Article 19’s nine principles of FOI legislation.

This qualitative study used semi-structured interviews to collect data from six experts selected by means of the snowball sampling technique and content analysis. The study used a modified Delphi design consisting of two rounds of interviews.

The results showed that little effort is made by government officials to demonstrate commitment to the implementation of FOI legislation.

The passing of FOI is expected to reduce corruption, increase public participation, reduce the level of secrecy and increase transparency and openness. This is not the case as the implementation of this socioeconomic right in South Africa is faced by numerous challenges, such as a lack of political will, secrecy laws providing for the opposite of what the FOI legislation seeks to achieve, poor legislative interpretation and a lack of clear policies. The study proposes a framework aimed at addressing these challenges.

The study provides a framework for the implementation of FOI legislation. The framework was developed under the guidance of Article 19 principles of freedom of information legislation.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

The paper examines the key driving factors behind the rapid and uneven growth of social enterprises in China based on Kerlin’s Macro-Institutional Social Enterprise (MISE) model of social enterprise development, with an emphasis on testing key local institutional factors.

The study adopts the quantitative method approach. The hypotheses have been tested based on a cross-regional empirical analysis with two national datasets on China.

This study shows that among the state, market and civil society, local government support in terms of favorable policies is the sole determinant factor driving China’s social enterprise growth. On the other hand, the market is irrelevant and local civil society impedes social enterprise growth. This demonstrates that the current growth model is the result of government intervention.

The datasets have a limited sample size. We suggest that future studies may collect a larger sample size with more comprehensive information. We think this study will encourage more comparative qualitative studies at the local level to reveal the underlying mechanisms of growth.

Since government policy is the determinant factor, the quality and quantity of government-backed incubation programs and platforms would matter the most for social enterprise growth. Our study also helps social entrepreneurs understand what factors matter when they try to develop social enterprises in China. They are advised to work on aspects of gaining legal legitimacy and political support in order to grow the sector.

This conclusion suggests that professionals and practitioners should review the implications of the current growth of social enterprises in China, in terms of their sustainability, given their institutional isolation from other sectors.

Current studies have yet to thoroughly explore the role of meso- and micro-institutional factors in social enterprise development, especially in different contexts. With reference to Kerlin’s framework and the tri-sector model, this paper advances the understanding of social enterprise growth in China.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

While a substantial amount of research has been conducted in western societies exploring public attitudes toward police (ATP) among immigrants in recent decades, the question of how recently arrived immigrants view the police in Asian societies has been largely overlooked. This study aims to explore Southeast Asian immigrants' ATP in Taiwan and how assimilation, discrimination, affirmation, procedural justice, bifocal lenses and contact experiences – viewed simultaneously – impact their perceptions.

Using a combination of convenience and snowball sampling methods, a total of 579 completed survey responses were collected in Taiwan with a response rate of 89%. Structural Equation Modeling (SEM) was used to examine key factors that impact immigrants' attitudes toward the Taiwanese police.

The findings suggest that procedural justice and assimilation are two robust and direct predictors of immigrants' attitudes toward Taiwanese police. Immigrants from Southeast Asian countries who perceive that they have been treated fairly by Taiwan police tended to report more positive ATP. Likewise, higher levels of assimilation boosted confidence in the police. In addition, both nationality and marital status had a significant impact on perceptions of the police.

This pioneering study examines immigrants' ATP among four groups of Southeast Asians in Taiwan —namely, immigrants from Indonesia, Vietnam, Thailand and the Philippines. The use of SEM strengthens the robustness of the findings derived from this study.