dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the interviewees (hereafter referred to as I1 to I80) were invited face-to-face followed by e-mail invitation, with a 75 percent response rate. Finally, to avoid elite bias  (Myers and Newman, 2007)  and to present different and richer perspectives on the phenomenon under investigation, we combined project managers, team members, sponsors, and other stakeholders. Then, data were collected via synchronous chat meetings and video recorded via the Zoom® virtual chat platform. Primary data were collected in Brazilian Portuguese, the native language of all the interviewees, and then translated and transcribed into English. Direct contact was avoided due to the sanitary restrictions regarding the Covid-19 pandemic, currently in one of the many variations. Therefore, there were no significant distracting noises or eventualities registered, such as interruptions, for instance.
Finally, the interviews lasted 30 minutes on average. Next, the raw data was coded in waves, following Miles, Huberman, and Saldaña (2014) and  Saldaña (2013). Direct quotations were authorized, and the identities were preserved for ethical issues. Finally, the raw data were analyzed through content and causal chain analysis, following Schreier (2012) and the native language, transcribed, and then translated into English.
IV. Findings and Analysis In this section, we analyzed the findings of the interviews after carefully investigating the raw data. Figure  1  illustrates the word frequency: Note in Figure  1  that Violation of Trust is associated with words like "loss," "credibility," "mistrust," and "Violation," among the most frequent. In addition, Figure  2  displays the word cloud of the raw data as follows:  Observe in Figure  3  that "loss," "loss of credibility," and "negotiations" frequently emerged when the interviewees answered the question about the consequences of the Violation of Trust for project management negotiations in the short term. Figure  4  illustrates the consequences for Trust violation in the long-term, as follows: Figure  4  Long-Term Word Cloud. Source: Database Note in Figure  4  that "credibility," "loss of credibility," "negotiations," "future business," and "mistrust" frequently emerged when the interviewees answered the question about the consequences of the Violation of Trust for project management negotiations in the long term.
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Community-run journals using the free OJS platform are overwhelmingly concentrated in the Global South, but the chemical and physical sciences combined make up ∼1% of these journals by discipline  (Khanna et al. , 2022). Our suite of report types was designed to facilitate participation by recognizing a more inclusive range of contributions. We support multi-language abstracts and encourage authors to provide one or more translations to support local interest in regions covered by their studies. Seismica's Equity, Diversity, and Inclusion (EDI) team has the mandate to be everywhere at once -members are distributed on key task forces. Although these individuals have EDI as their formal mandate at Seismica, everyone in the organization shares responsibility for pursuing these ideals and learning from the experiences of others.
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Where current out-of-the-box OJS functions did not fulfil our needs, our Tech team created purpose-built tools. For example, OJS cannot assign expertise tags to registered users. To help Handling Editors find reviewers, we built an independent reviewer database that allows individuals to register using their Open Researcher and Contributor IDentifier (ORCID)  19  and tag their expertise within Seismica's scope 20. These pre-defined tags were derived from the keywords mentioned in the journal scope. Our database enables Handling Editors to find qualified reviewers more easily, and contact them based on their ORCID or email address. To ensure privacy protection and GDPR 21  compliance, we do not currently collect personal and/or demographic data that is not already publicly displayed on a user's ORCID profile, except for an email address that a user can optionally provide.
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Post-launch insights Seismica has only just launched. To date, we have been open for submissions for about 120 days. We have received on average 2-3 submissions per week. Seismica has been well received in the community, following our high levels of community engagement with our social media and conference presentations. Given these factors, together with a strong uptake with both our mailing list and reviewer database, we consider the launch of Seismica to have been highly successful. There are several early and important insights we have gained from this experience. Our research community values community-led scholarly publishing. The opportunity to influence the journal incentivizes volunteering efforts. Seismica responds to community needs in all aspects of our design. Our organization is growing according to our mission and philosophy of non-profit motive.
The success of our launch was supported by the professional reputations of our volunteers (Appendix A), and we have relied on their name recognition to build our own. Seismica will establish itself as a brand that researchers are eager to affiliate with using these approaches: a rigorous and supportive review process led by disciplinary experts will ensure that our published papers are of high quality; the Seismica Ambassador program will inform the broader community of our mission and the issues in the scholarly publishing industry that we can address through community-led publishing; we will continue to visibly evolve our policies to stay on top of best practices in Open Science, EDI, and community needs. Second, we must be prepared to grow and sustain our community. The excitement of journal-building has motivated generous contributions of time and labor from a large number of people.
Organizational Structure Team This team is responsible for completing and maintaining the organizational structure of Seismica through policy development and review. Media & Branding Team Member: Carry out the work ensuring the success of Seismica's branding strategy, led by the Team Chair in discussion with Team Members. Members are organized in task forces focusing on: Meetings & Conferences, Seismica Ambassadors Program, Social Media Communications, Upcoming Publications and News, and Creative Content & Messaging. Task forces consist of a lead and a support member. Weekly meetings of the entire Team and coordination with the Management Committee ensure continuity. Tech Team Chair: Handle incoming requests for additions/changes on the Seismica website, the editorial system, and the reviewer database.
Establishing and communicating criteria for publishability, providing rapid reviews and seeking external reviewers, author commu-nications, maintaining fast workflows. Fast Reports Team Member: Handle submissions, provide rapid review of the submissions, assess state of revised manuscripts and make decisions, communicate with authors regarding process and decisions, contribute to the Fast Reports activities. Attend team meetings. Editorial Mentoring: Provide ad hoc advising on editorial decisions to support Handling Editors, participate in the Organizational Structure design process, develop editor and reviewer best-practices and trainings, coordinating Mentoring Committee meetings and reporting back to the Seismica Board. Handling Editors: Quality control of manuscripts, establish and maintain contact with reviewers, communicate with authors regarding process and decisions, handle conflicts in reviewers' opinions, assess state of revised manuscripts and make decisions, upload reviews report to website, attend Board meetings.
Acknowledgements The volunteer Board of Seismica (listed in Appendix A) is grateful for the support we have received from the research community,  McGill University Libraries , and one another during the ongoing adventure and challenge of building a community-led journal. We recognize there are significant challenges ahead, challenges that face the global research community on a much broader scale, but to whose resolution we can make a small contribution. We are enthusiastic about the work ahead and invite you to join us! We thank the members of the original Seismica Task Force who initiated this effort (van den Ende et al. , 2021), and the  leadership of Volcanica and Tektonika  for inspiration, sharing knowledge, and support. We thank PKP and  McGill Library. We thank the broader  Seismica community , including participants in our Slack group, researchers who engage with us at conferences and on social media, those who have added themselves to our reviewer database, and members of our mailing list.
Actuaries model insurance claim amounts using heavy tailed probability distributions. They routinely need to evaluate quantities related to these distributions such as quantiles in the far right tail, moments or limited moments. Furthermore, actuaries often resort to simulation to solve otherwise untractable risk evaluation problems. The paper discusses our implementation of support functions for the Feller-Pareto distribution for the R package actuar. The Feller-Pareto defines a large family of heavy tailed distributions encompassing the transformed beta family and many variants of the Pareto distribution.
The package also introduces support functions to compute raw moments, limited moments and the moment generating function (when it exists) of continuous distributions. These functions all prove useful in loss modeling and risk evaluation. We introduce with this paper our latest addition to the range of heavy tailed probability models supported by actuar: the Feller-Pareto distribution and related Pareto distributions with a location parameter. To the best our knowledge, the Feller-Pareto distribution has not yet been extensively used for insurance loss modeling. However,  Brazauskas (2002)  proposes its use to model the size of insurance claims and  Cummins, Dionne, McDonald, and Pritchett (1990)  relies on a special case to estimate the tail distribution and reinsurance premiums for fire claims.
The paper is organized as follows. For the reader not familiar with the Feller-Pareto family of distributions, we first summarize how it extends the transformed beta family in Section 2. Next, we discuss the theoretical and numerical challenges faced during the numerical implementation in Section 3. In particular in Section 3. 2, we also compare various algorithms to generate random variates from the Feller-Pareto and related distributions. In Section 4, we provide a small case study based on real world data. The workhorses behind the R support functions are implemented in C for unrivaled speed and efficiency. We close the paper by briefly explaining how R package developers can now take advantage of these routines through actuar's new package API in Section 5. For the purpose of this paper, a heavy tailed distribution is a positive distribution without a finite moment generating function.
R implementation As already mentioned in the introduction of this paper, R includes functions to compute the PDF, the CDF and the quantile function of standard probability laws, as well as functions to generate variates from these laws. For some root foo (e. g. , pois for the Poisson distribution or gamma for the gamma distribution), the support functions are named dfoo, pfoo, qfoo and rfoo, respectively. Since version 0. 9-4, released in 2007, the actuar package provides d, p, q and r functions for all the probability laws useful for loss severity modeling found in Appendix A of  Klugman et al. (2019)  and not already present in base R, excluding the log-t but including the Loggamma distribution of  Hogg and Klugman (1984). Version 2. 0-0 of the package also added support for the Inverse Gaussian distribution.
The package vignette "distributions" keeps the up-to-date list of supported distributions and root names of the R functions. In addition to the d, p, q and r functions, actuar provides m, lev and mgf functions to compute, respectively, theoretical raw moments m k , theoretical limited moments E (X ∧ x) k and the moment generating function The root of the support functions are, respectively, fpareto, pareto4, pareto3 and pareto2. Therefore, for the Feller-Pareto, the package now provides the functions dfpareto, pfpareto, qfpareto, rfpareto, mfpareto and levfpareto. Table  1  shows the correspondence between the parameters of the Feller-Pareto as defined in Section 2. 1 and the arguments of the R functions. M X (t) = E e tX , r r r r r r r r j T © c r r r r r r r r r j c % T c c c r r r r r r r r j c c ' E r r µ = 0 Feller-Pareto µ, α, γ, τ, θ τ = 1 Pareto IV µ, α, γ, θ γ = 1 τ = 1 Pareto II µ, α, θ α = 1 Pareto III µ, γ, θ µ = θ Pareto I α, θ µ = 0 Transformed Beta α, γ, τ, θ α = 1 Inverse Burr γ, τ, θ γ = τ Inverse Paralogistic τ, θ γ = 1 Inverse Pareto τ, θ γ = 1 Generalized Pareto α, τ, θ τ = 1 Pareto α, θ τ = 1 Burr α, γ, θ γ = α Paralogistic α, θ α = 1 Loglogistic γ, θ α = 1 γ = 1 τ = 1 In the sequel, we only discuss the implementation of the Feller-Pareto functions since the Pareto IV, III and II functions are merely special cases.
These performance measurements carry over to the generation of Feller-Pareto variates, as shown in Table  3. (We obtained benchmarking times with the very convenient function benchmark of package rbenchmark  (Kusnierczyk 2012). ) The other issue we investigated was performance of the algorithms in the main limit cases. First, when α → 0 and τ → ∞, variates from a B(τ, α) tend to 1. Due to the negative power in its second step, Algorithm 3 tends, in such a situation, to yield indeterminate values -"not a number", or NaN in R terminology. Algorithm 2 does not exhibit this behavior. Figure  3  (left graphic) shows the effect on the empirical CDF of samples generated with Algorithms 2 and 3 for a small value of α and a large value of τ. Algorithm 2 is clearly preferable. The indeterminate value issue of Algorithm 3 also arises in Algorithm 1 when α → τ → 0 and  both gamma variates may be numerically equal to 0.
Of more interest here is the case where b < 0, b ̸ = -1, -2,. and a > 1 + ⌊-b⌋. Integration by parts of (18) yields Now, let B(a, b; x) = Γ(a + b) x 0 t a-1 (1 -t) b-1 dt, ( 18 B(a, b; x) = -Γ(a + b) x a-1 (1 -x) b b + (a -1)x a-2 (1 -x) b+1 b(b + 1) + • • • + (a -1) • • • (a -r)x a-r-1 (1 -x) b+r b(b + 1) • • • (b + r) + (a -1) • • • (a -r -1) b(b + 1) • • • (b + r) Γ(a -r -1) × Γ(b + r + 1)I(a -r -1, b + r + 1; x), ( 19 ) where r = ⌊-b⌋. Following the terminology of actuar, we will call (  18 ) the beta integral. (It is also known in the literature as the normalized incomplete beta function. ) The package includes a C routine betaint -and a non exported R interface of the same name -that is a rather straightforward C implementation of (  19 ). Its sole usage in the package is to evaluate the limited moments of distributions of the Feller-Pareto family.
Case study We illustrate in this section how the Feller-Pareto distribution -and the functions we provide in actuar -can prove useful to model insurance loss costs, evaluate premiums and simulate data. To this end, we use a dataset well known in actuarial circles as the Danish dataset. Collected by Copenhagen Reinsurance, it consists of aggregate claim amounts for 2,492 fire losses over     (1997); McNeil (1997) ;  Resnick (1997) ;  Davison (2003). The R package SMPracticals  (Davison 2019)  provides the Danish dataset in the time series object danish. Since the dates of the events are irrelevant in our analysis, we use the data as a simple atomic vector. R> data("danish", package = "SMPracticals") R> danish. loss <-as. numeric(danish) As Figure  6  shows, the Danish loss amounts exhibit a heavy, Pareto type, tail.
Loss modeling The first step in our case study will be to fit the Feller-Pareto distribution to the data using maximum likelihood estimation (MLE). Our main R tool will be function fitdist from package fitdistrplus (Delignette-Muller and Dutang 2015), which wraps and extends the well known function fitdistr from package MASS  (Venables and Ripley 2002). Since the Danish dataset contains positive loss amounts, we have a natural value of µ = 0 for the location parameter of the Feller-Pareto. We could try to estimate this parameter along with the shape parameters and the scale parameter, but this proves numerically cumbersome. Brazauskas (2002)  provides explicit formulas for the Fisher information matrix when the location parameter is set to 0. However, for the current example, we simply rely on the numerical values provided by the R optimization routines.