In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves $11.7$ F1 than previous parallel models and up to $5.1$ F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.

An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction | VIDEO

Approximate number perception is noisy, but it is unclear whether that noise reflects an error-prone attempt to report one's best guess of the value, or an inherently stochastic process such as sampling. Here we demonstrate that approximate number estimation should be thought of as a sampling procedure, as the average of two estimates of the same stimulus tends to outperform either estimate alone. Additionally, the average difference between the two estimates of a given number linearly increases as a function of number, consistent with Weber's law. Finally, we provide evidence that the classic psychophysical curves associated with number representation are represented within the mind after a single brief viewing of a stimulus, and not only useful for describing a summary of behavior over many trials.

Sampling in Approximate Number Perception

We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on $\W$-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally, we discuss why such a result is not easily extendable to other HGs classes even in this promising scenario. Namely, we establish a purely computational property necessary for achieving PAC stability.

PAC learning and stabilizing Hedonic Games: towards a unifying approach.

We study the properties of elections that have a given position matrix (in such elections each candidate is ranked on each position by a given number of voters).  We show that counting elections that generate a given position matrix is #P-complete. Consequently, sampling such elections uniformly at random is challenging and we propose a simple algorithm that does not offer guarantees on the distribution. Next, we consider the problem of testing if a given matrix can be implemented by an election with certain structure (such as single-peakedness or group-separability). Finally, we consider the problem of testing if a given position matrix can be implemented by an election with a Condorcet winner.  We complement our theoretical findings with experiments.

Properties of Position Matrices and Their Elections

We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test accuracy on a validation set, that may not exist in practice. In this work, we focus on task-agnostic data valuation without any validation requirements. The data buyer has access to a limited amount of data (which could be publicly available) and seeks more data samples from a data seller. We formulate the problem as estimating the differences in the statistical properties of the data at the seller with respect to the baseline data available at the buyer. We capture these statistical differences through second moment by measuring diversity and relevance of the seller’s data for the buyer; we estimate these measures through queries to the seller without requesting the raw data. We design the queries with the proposed approach so that the seller is blind to the buyer’s raw data and has no knowledge to fabricate responses to the queries to obtain a desired outcome of the diversity and relevance trade-off. We will show through extensive experiments on real tabular and image datasets that the proposed estimates capture the diversity and relevance of the seller’s data for the buyer.

Fundamentals of Task-Agnostic Data Valuation

We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, namely the Nash social welfare (NSW) function. This corresponds to equating algorithm's performance to the geometric mean of its expected rewards and leads us to the study of  {\it Nash regret}, defined as the difference between the---a priori unknown---optimal mean (among the arms) and the algorithm's performance. Since NSW is known to satisfy fairness axioms, our approach complements the utilitarian considerations of average (cumulative) regret, wherein the algorithm is evaluated via the arithmetic mean of its expected rewards. 

This work develops an algorithm that, given the horizon of play $T$, achieves a Nash regret of $O \left( \sqrt{\frac{{k \log T}}{T}} \right)$, here $k$ denotes the number of arms in the MAB instance. Since, for any algorithm, the Nash regret is at least as much as its average regret (the AM-GM inequality), the known lower bound on average regret holds for Nash regret as well. Therefore, our Nash regret guarantee is essentially tight.  In addition, we develop an anytime algorithm with a Nash regret guarantee of $O \left( \sqrt{\frac{{k\log T}}{T}} \log T \right)$.

Fairness and Welfare Quantification for Regret in Multi-Armed Bandits

The conditional randomization test (CRT) was recently proposed to test   whether two random variables $X$ and $Y$ are conditionally independent given confounding variables  $Z$. The CRT assumes that the conditional distribution of $X$ given $Z$ is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data. The aim of this paper is to develop a novel  alternative  of  CRT   by using  nearest-neighbor sampling without assuming  the exact form of the distribution of $X$ given $Z$. Specifically, we  utilize the computationally efficient 1-nearest-neighbor to approximate the conditional distribution that encodes the null hypothesis. Then, theoretically, we show that the distribution of  the generated samples is very close to the true conditional distribution in terms of  total variation distance.  Furthermore, we take the classifier-based conditional mutual information estimator as our test statistic. The test statistic as an empirical fundamental information theoretic quantity  is  able to well capture  the conditional-dependence feature. We show that our proposed  test    is computationally very fast, while controlling type I and II errors quite well. Finally, we demonstrate the efficiency of our proposed test in both synthetic and real data analyses.

Nearest-Neighbor Sampling Based Conditional Independence Testing

**Objective** The peer review system used by academic journals
to assess the quality of submitted papers is constantly in need
of new technologies that can help reviewers and increase the
objectivity of the process. The aim of the present study was to
develop a comprehensive list of words related to research
methods and statistics, which could then be used to
automatically study the content and the quantity and types of
methodological and statistical issues identified in peer review
reports.

**Design** The terms for the list were identified from different
glossaries.1,2 The terms were organized into subcategories,
some of which were more related to methodological aspects
(eg, study design, sampling, or procedures) or statistical
aspects (eg, parametric descriptive parameters or data
presentation) of research. The list was refined and enriched
with terms using a computational language model. The final
list and the preregistration can be found at the Open Science
Framework website (https://osf.io/d34b9/). The occurrence
of terms in the PEERE database3
of peer review reports, for
which data on reviewer gender, continental region, review
recommendation, journal impact factor, and subject area (N
= 496,928) were available, was counted. Regression models
were created to determine the frequency of the terms used
based on peer review characteristics.

**Results** The mean length of a peer review was 196 words
(95% CI, 108-330 words). A total of 26.8% of review reports
in the sample contained at least 1 methodological term, while
70.7% contained at least 1 statistical term. It was more likely
that the review contained methodological terms if the
reviewer was female and resided in North America or Oceania
compared with other continents, if the recommendation was
to reject the article or send it for major revisions, if the
journal was from the social sciences and humanities, if the
journal was in the top impact factor quartile, and if the article
was a longer peer review. More words related to statistics
were observed when the reviewer was female, for reviews
recommending any revisions, for journals from health and
medical sciences, in journals in the top impact factor quartile,
and in longer reviews. Mixed regression models indicated
that the scientific area was more strongly associated with
words related to statistics than were reviewer
recommendations.

**Conclusions** The application of this newly developed list
showed that methodological and statistical terms are not a
common topic in peer review and that they are not always
present in review reports from different fields. The list
successfully covered research terms because the most-
identified terms were related to categories relevant for most
types of research. Future analysis should try to understand
the context of the occurrence of words from the list to confirm
the validity of the list of terms.

**References**

1. Stark PB. Glossary of Statistical Terms. Accessed March 23,
2021. https://www.stat.berkeley.edu/~stark/SticiGui/Text/
gloss.htm
2. Framework for Open and Reproducible Research Training.
The Glossary of Terms. Accessed July 12, 2021. https://docs.
google.com/document/d/1oNsHSRmgAsDGUKvq5joqBpv
HeRmDJKldH2kSyJovHhk/edit#heading=h.2l8ssl89e1ey
3. Squazzoni F, Ahrweiler P, Barros T, et al. Unlock ways to
share data on peer review. Nature. 2020;578(7796):512-514.
doi:10.1038/d41586-020-00500-y

**Conflict of Interest Disclosures** Ana Marušić is a member of
the Peer Review Congress Advisory Board by was not involved in the
editorial review or decision for this abstract. No other disclosures
reported.

**Funding/Support** This study was funded by the Croatian Science
Foundation “Professionalism in Health–Decision making in
practice and research” under grant agreement IP-2019-04-4882.
Richard A. Klein received funding from a Replication Studies grant
(401.18.053) from the Netherlands Organization for Scientific
Research, and a Consolidator Grant (IMPROVE) from the European
Research Council (grant 726361). Francisco Grimaldo and Daniel
Garcia-Costa are partially supported by the Spanish Ministry of
Science, Innovation and Universities, the Spanish State Research
Agency, and the European Regional Development Fund under
project RTI2018-095820-B-I00.

**Role of the Funder/Sponsor** The funders had no role in the
design and conduct of the study; collection, management, analysis,
and interpretation of the data; preparation, review, or approval of
the abstract; and decision to submit the abstract for presentation.

![](https://assets.underline.io/uploads/markdown_image/1/image/eba25e8a44f7081b702973e5ddf9a854.png)

Development of a List to Detect Statistical and Methodological Terms in Peer Reviews: A Cross-Sectional Study

**Objective** As gatekeepers for knowledge production, editors
use their backgrounds to determine what counts as high-
quality research, how research should be done, and whether it
offers novel insights. Individuals who come from racialized
backgrounds are often excluded from these conversations,
ensuring the publishing world continues to be led by the
perspectives of the dominant racial group.1
To address this
issue, the journal Teaching and Learning in Medicine (TLM)
created an internship program for early career scholars who
self-identified as Black, Latinx, or Indigenous to gain
experience in the publishing/editorial process.2
The purpose
of this study was to critically examine the outcomes of the
first 6 months and investigate its implications for other
journals interested in similar efforts.

**Design** A collaborative autoethnographical study was
designed to collectively analyze the program. Editorial staff
and interns reflected on their lived experiences participating
in the internship to understand the current culture of
academic publishing and explore how to improve.3
Data
sources included archival emails and program planning
documents, focus group data, and group exercises. After
transcribing the focus groups, data were analyzed using thematic analysis by both editorial staff and interns.
Institutional review board approval was not required for this
study.

**Results** Focus group data indicated that while TLM designed
the program with antiracist intent, mentors did not
foreground their race in the editorial/publishing process
outside of the intern’s minoritized status as a program
selection criterion. Early on, mentors viewed the internship
more as an opportunity to improve the journal’s editorial
work, for participants to gain experience working in a
historically guarded space, and to contribute to achieving
racial equity in medical education. Therefore, despite the
program’s specific antiracist focus, 6 months in, none of the
mentors (who identified as White individuals) had explicitly
discussed topics of race with their interns. Mentors’ reasons
for not discussing race varied, including uncertainty about
how to invite interns into such a discussion and not seeing
interns as racialized individuals. However, at the end of the
6 months, researchers realized the need to discuss this topic,
thus moving the program into explicit conversations about
race and the role it plays in publishing.

**Conclusions** Although the program met some antiracist
goals, stakeholders did not explicitly discuss the role that race
plays in the review process, therefore limiting the program’s
initial impact. Through this collaborative autoethnography,
TLM stakeholders critically reflected on the program in real
time and addressed this gap. In doing so, they engaged in the
ongoing critical action needed to support equity within the
editorial process. Editorial staff at TLM now have the
opportunity to address ongoing power dynamics between
interns and staff members that will advance the journal’s
efforts at antiracism. Plans are currently underway to ensure
interns’ experiences are incorporated.

**References**

1. Salazar JW, Claytor JD, Habib AR, Guduguntla V, Redberg
RF. Gender, race, ethnicity, and sexual orientation of editors
at leading medical and scientific journals: a cross-sectional
survey. JAMA Intern Med. 2021;181(9):1248-1251.
doi:10.1001/jamainternmed.2021.2363
2. Cianciolo AT. Letter from the editor—Teaching & Learning
in Medicine’s anti-racism strategy. Teach Learn Med.
2020;32(5):457-458. doi:10.1080/10401334.2020.1836461
3. Chang H, Ngunjiri FW, Hernandez KAC. Collaborative
Autoethnography. Left Coast Press; 2013.

**Conflict of Interest Disclosures** None reported.

**Disclaimer** This work was prepared by a military or civilian
employee of the US government as part of the individual’s official
duties and therefore is in the public domain. The opinions and
assertions expressed herein are those of the author(s) and do not
necessarily reflect the official policy or position of the Uniformed
Services University or the US Department of Defense.

**Additional Information** Anabelle Andon is a co–corresponding
author.

![](https://assets.underline.io/uploads/markdown_image/1/image/a8d384158802bc2cfad5c9d7e26fb85b.png)

Qualitative Assessment of an Antiracism Editorial Internship Program for Early Career Underrepresented Scholars at Teaching and Learning in Medicine

**Objective** To analyze the proportion of spin strategies
present in randomized clinical trials (RCTs) published in
high-impact journals appraised at low risk of bias.

**Design** For this cross-sectional study, a comprehensive
search was made in Ovid MEDLINE, retrieving all RCTs
published in the 10 highest-impact medical journals in
medicine and surgery (by 2018 Journal Citation Reports
impact factor) from January 1, 2018, to February 28, 2020.
Stratified sampling was then performed of 150 articles,
adjusted by journal. Then, the risk of bias of each RCT was
assessed independently and in duplicate using the Cochrane
risk-of-bias tool. For each included article, an adaptation of
the classification scheme created by Boutron et al1
for RCTs
was used to identify spin strategies in every section of the
article.

**Results** A total of 46 RCTs at low risk of bias were appraised
using the spin classification scheme. From those included in
the analysis, 25 studies were published in internal or general
medicine journals and 21 in surgery journals. The number of
patients included in the RCTs ranged from 20 to 12,000. The
most spin was identified in the discussion and conclusion
sections, with the most common strategies being focusing on
statistically significant secondary outcomes (4 [8.7%]), ruling
out adverse events (4 [8.7%]), acknowledging statistically
nonsignificant results for the primary outcome but
emphasizing the beneficial effect of treatment (4 [8.7%]), and
focusing only on statistically significant results (5 [10.9%]). In
subgroup analyses, a total of 38 spin strategies were identified
in studies with nonsignificant primary outcomes vs 11 in
studies with significant results. Assessment according to
specialty identified 35 spin strategies in surgery journals and
14 in internal medicine journals.

**Conclusions** Overall, the use of spin strategies was found to
be more common in studies with a statistically nonsignificant
primary outcome vs those with a significant primary outcome.
This study highlights the need to raise awareness about the
use of spin, even in studies published in high-impact journals
and at low risk of bias, as the use of these strategies can affect
readers’ decision-making.

**Reference**

1. Boutron I, Dutton S, Ravaud P, Altman DG. Reporting and
interpretation of randomized controlled trials with
statistically nonsignificant results for primary outcomes.
JAMA. 2010;303(20):2058-2064. doi:10.1001/jama.2010.651

**Conflict of Interest Disclosures** None reported.

![](https://assets.underline.io/uploads/markdown_image/1/image/5451b89b7d6c71966bd2b23f3e7d34c2.png)

Spin in Randomized Clinical Trials of Top Medical Journals

**Objective** To characterize clinical and translational science
publications retracted for reasons related to the capture,
management, or analysis of data to better understand errors
that may occur in the research pipeline.

**Design** This scoping review complied with Preferred
Reporting Items for Systematic Reviews and Meta-analyses
(PRISMA) guidelines and followed a preregistered protocol.
The Retraction Watch database was queried through March
12, 2020.1,2 Records were eligible for abstract review if they
were published between 2010 and 2020, had a subject list
containing terms related to clinical and translational science,
and were retracted because of concerns with the capture,
management, or analysis of data. Abstracts were reviewed in
duplicate. A retracted article was eligible for full-text review if
abstract review determined it was published in English and related to clinical and translational sciences. During full-text
review, the study team extracted information on number of
authors, author attributions, data types and sources, study
design, statistical analysis plan, software, and data
availability. Research electronic data capture (REDCap) was
used for import of publication information from Retraction
Watch and data entry throughout the abstract and full-text
review processes.3
A random sample of 5% of articles were
reviewed in duplicate. Descriptive analyses were performed
using R, version 4.0.1 (https://www.R-project.org/).

**Results** Of 21,252 records retrieved from Retraction Watch,
1266 (6%) were eligible for abstract review and 884 (4%)
were eligible for full-text review. Of the 884 publications
eligible for full-text review, 786 (89%) were available online
through Northwestern University’s library system and
included in the final analyses. The analytic set included 571
articles (73%) involving human research, 213 reports (27%) of
animal research, and 47 systematic reviews or meta-analyses
(6%). Few retracted articles described data that were publicly
available (67 [9%]) or stated that data were available on
request (21 [3%]); most articles contained no statements
related to data availability or methods for data capture and
management. More than one-third of the retracted articles
(300 [38%]) did not specify the statistical analysis software
used. The most-used programs were SPSS/PASW (229
[29%]) and GraphPad PRISM (86 [11%]). Statistical software
such as Stata (43 [5%]), SAS (38 [5%]), or R/R Studio (29
[4%]) were infrequently reported.

**Conclusions** This scoping review identified more than 800
articles in clinical and translational sciences retracted over 10
years for concerns related to data capture, management, or
analysis. The results describe this cohort of retracted articles.
Future work will include comparisons with a set of randomly
selected publications that have not been retracted but are
matched on journal and time frame. Authors can improve the
rigor of scientific research by reporting software used and
data availability. Publishers, editors, and peer reviewers can
contribute to these improvements by advocating for
widespread adoption of transparent documentation.

**References**

1. Baldridge AS, Welty LJ, Rasmussen L, Whitley E. Analysis
of Retraction Watch Database for Retractions Due to Errors
in the Clinical Science Reproducible Research Pipeline: A
Scoping Review Protocol. DigitalHub, Galter Health Sciences
Library & Learning Center; 2020. doi:10.18131/g3-pep3-ky27
2. Retraction Watch database. Center for Scientific Integrity.
2018. Accessed March 12, 2020. http://retractiondatabase.
org/
3. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde
JG. Research electronic data capture (REDCap)—a metadata-
driven methodology and workflow process for providing
translational research informatics support. J Biomed Inform.
2009;42(2):377-381. doi:10.1016/j.jbi.2008.08.010

**Funding/Support Research** reported in this abstract was
supported, in part, by the National Institutes of Health’s National
Center for Advancing Translational Sciences (UL1TR001422).

**Role of the Funder/Sponsor** The National Institutes of Health
had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation,
review, or approval of the abstract; and decision to submit the
abstract for presentation.

**Disclaimer** The content is solely the responsibility of the authors
and does not necessarily represent the official views of the National
Institutes of Health.

**Acknowledgments** We thank the Retraction Watch team as well
as Q. Eileen Wafford (Galter Health Sciences Library, Northwestern
University).

![](https://assets.underline.io/uploads/markdown_image/1/image/6c6b7d09209a92da5307dff0d0537e5f.png)

Characteristics of Articles in Clinical and Translational Sciences Retracted for Reasons Related to the Capture, Management, or Analysis of Data: A Scoping Review

**Objective** A constructive peer review can bring a fresh
perspective to help improve an author’s work. Until recently,
there has been little guidance on what constitutes a good or
poor peer review and no empirical study about researchers’
experiences with unhelpful or upsetting reviews.1-3 This pilot
study aimed to explore the types of poor peer review practices
(PPRP) experienced by authors and assess their association
with authors’ ability to disseminate research.

**Design** An anonymous 51-question survey was designed
through Qualtrics. An invitation to complete the survey was
emailed to a random sample of 500 researchers funded by the
National Institutes of Health in 2018 (with the replacement of
undeliverable email addresses) and posted to a bioethics
discussion forum (mcw-bioethics@mailman.mcw.edu) with
approximately 600 members. Three mailings were performed
between April 5 and April 19, 2022. The study was
determined to be exempt by the University of Pennsylvania
institutional review board. A 28-item list of PPRP was
developed following a literature review; respondents’
experiences were assessed with yes-no and open-ended
questions. The PPRP was assessed through author responses
to Likert scale questions. Exploratory nonparametric analyses
of respondents’ reported experiences and demographic
characteristics were performed using Stata, version 12.1
(StataCorp LLC). Data were reported according to the
Consensus-Based Checklist for Reporting of Survey Studies
(CROSS).

**Results** A total of 112 researchers completed the surveys,
approximately 10% of those solicited. Respondents were
predominantly male (59 of 109 [54%]), held a PhD (87 of 112
[78%]), were older than 50 years (73 of 111 [66%]), identified
as White race (92 of 106 [87%]), published more than 50
peer-reviewed papers in their career (71 of 111 [64%]), were
trained in the humanities or social sciences (61 of 112 [54%]),
and conducted primarily empirical research (68 of 111 [61%]).
The mean number of PPRP experienced per author was 12.5
of 28 (44.6%) (range, 0-27; 95% CI, 11.2-13.8). A total of 57%
(63 of 111) of authors admitted abandoning a manuscript after
receiving unfair peer reviews; 67% (74 of 111) of authors
sometimes or often received insightful peer reviews that
improved the quality of their final papers. An exploratory
univariate analysis of the association between the total
number of PPRP reported per author and demographic
characteristics and reported positive peer review experiences
is presented in **Table 62**. This analysis suggests areas for
future study, particularly the need to explore types of helpful
reviews.

![](https://assets.underline.io/uploads/markdown_image/1/image/774e36d00993b4c0095a2e8844e439fc.png)

**Conclusions** This pilot study was the first to date to assess
researchers’ experiences with PPRP. The low response rate
suggests high selectivity and bias and that respondents report
experiences with an extensive range of PPRP. The study lays
the groundwork for future research, which may be valuable
for improving peer review quality.

**References**

1. Smith R. Peer review: a flawed process at the heart of
science and journals. J R Soc Med. 2006;99(4):178-182.
doi:10.1258/jrsm.99.4.178
2. Mavrogenis AF, Quaile A, Scarlat MM. The good, the bad
and the rude peer-review. Int Orthop. 2020;44(3):413-415.
doi:10.1007/s00264-020-04504-1
3.Silbiger NJ, Stubler AD. Unprofessional peer reviews
disproportionately harm underrepresented groups in STEM.
PeerJ. 2019;7:e8247. doi:10.7717/peerj.8247

**Conflict of Interest Disclosures** None reported.

![](https://assets.underline.io/uploads/markdown_image/1/image/fecf5282f0124e00949af5a137c941b1.png)



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An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction | VIDEO

Sampling in Approximate Number Perception

PAC learning and stabilizing Hedonic Games: towards a unifying approach.

Properties of Position Matrices and Their Elections

Fundamentals of Task-Agnostic Data Valuation

Fairness and Welfare Quantification for Regret in Multi-Armed Bandits

Nearest-Neighbor Sampling Based Conditional Independence Testing

Development of a List to Detect Statistical and Methodological Terms in Peer Reviews: A Cross-Sectional Study

Qualitative Assessment of an Antiracism Editorial Internship Program for Early Career Underrepresented Scholars at Teaching and Learning in Medicine

Spin in Randomized Clinical Trials of Top Medical Journals

Characteristics of Articles in Clinical and Translational Sciences Retracted for Reasons Related to the Capture, Management, or Analysis of Data: A Scoping Review

A Survey of Authors’ Experiences With Poor Peer Review Practices

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