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Record W2549419161 · doi:10.1111/geb.12546

In the company of greatness: announcing the best reviewers and best associate editors

2016· article· en· W2549419161 on OpenAlexaboutno aff
Brian J. McGill, María Dornelas, Richard Field

Bibliographic record

VenueGlobal Ecology and Biogeography · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsGreatnessBest practiceSet (abstract data type)Service (business)Quality (philosophy)Public relationsPsychologyLibrary scienceHistoryPolitical scienceMedia studiesSociologyLawComputer scienceBusinessMarketingSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

In our editorial in January 2016 (McGill et al., 2016), we announced our intention to give awards for ‘Best Reviewers’ and ‘Best Associate Editors’. Our thinking was that these roles are a lot of work, are critical to the journal, and are all too rarely recognized. We figured that by highlighting a few especially exceptional participants we could bring recognition to this role. Selecting just a few has not proved easy but we think it is important and we made the commitment, so here we announce the winners. At GEB, we try to look for ways to encourage good practice. We have decided to kick the reviewer and editor awards off with a double set of them, one for each of the last two years. Thus, one set of awards is for the period before we announced that there would be awards. We like the idea of rewarding altruism: people who do great service to the scientific community without any thought of being rewarded for it. We label the two time-periods 2014–2015 and 2015–2016 for convenience, though actually we had to go to press some months ago and operationally we are running an approximately July–June annual time-window from now on. We look forward to giving awards again for July 2016-June 2017 in the December 2017 issue of GEB. For the reviewer awards, we took into account the number and timeliness of reviews, consulted our Associate Editors on quality of reviews and checked out some of the reviews sent in by short-listed people. In the process we were reminded of what a great job many people do when reviewing for GEB. We, and the field in general, benefit greatly from thoughtful, constructive reviews from people who demand high standards and offer excellent suggestions about how to reach those heights. What these reviewers do reflects well on science and scientists. As is typical, there was a fair amount of inequity among reviewers. Some individuals frequently declined or did not answer invitations to review and yet continued to publish in GEB1. And of course there was a long tail of hundreds of individuals asked only once or twice. But other individuals were asked many times and accepted many times. Although most of these will not receive rewards, they are critical to our journal and we thank them annually as we do in this issue. And did so with great insight and quality. Recognizing this behaviour is in a nutshell is why we want to offer these rewards. It rapidly became clear that pulling out a few names from a long list of people who have provided great service is difficult. We have undoubtedly not rewarded some of our excellent reviewers. In the process of deciding on the award winners, we have however sent a long list of many dozens of names of noteworthy reviewers to more than 50 experts in our field (our Associate Editors), associating those names with great work. We hope this may end up producing its own rewards for some of you, even if you are not named in this document this year. We also found it difficult to decide the Associate Editor awards. We interact regularly with the editors, and did not need to do an information-gathering exercise. We compared thoughts between the three of us and also David Currie, the Editor-in-Chief until 2015. Choosing was especially challenging because, by definition, our associate editors are a select group who all do excellent work. The three people listed below have been outstanding for years, and all have served as GEB editors since 2011 or before. Thus duration of service proved a major criterion in these inaugural awards. The main message, though, is that we are extremely grateful for the superb work done by our entire body of Associate Editors. We are privileged to work with you all. To briefly grab your attention for another topic, we are pleased to report some other developments of GEB. In particular, we have recently published new author instructions, so please check them out. Among the changes is the introduction of a new type of paper, the data paper where we publish short summaries that introduce noteworthy data sets. To make clear that GEB welcomes papers distinguished by covering large spatial, taxonomic and/or temporal scales, we now require the structured abstract to contain short statements of coverage across all three of these scales. We have also upped the expectation for data sharing and publication, though we have held back from making it an absolute requirement. We now expect you to make the data publicly available, or else give a clear statement of why this was not possible. We are extremely grateful for all the hard work by hundreds of reviewers and dozens of associate editors that goes into our journal. They are the heart and soul of what makes our journal great. And we are not only immensely grateful but have genuinely enjoyed working with and learning from these scientists. And we can't wait to recognize more people next year! Brian J. McGill1, Maria Dornelas2 and Richard Field3 1University of Maine, School of Biology & Ecology and Mitchell Center for Sustainability Solutions, Orono, Maine, USA E-mail: [email protected] 2University of St. Andrews, St. Andrews, Fife, United Kingdom E-mail: [email protected] 3University of Nottingham, School of Geography, Nottingham, UK E-mail: [email protected]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.089
metaresearch head score (Gemma)0.292
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.911
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.292
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0100.010
Scholarly communication0.0430.023
Open science0.0070.008
Research integrity0.0210.037
Insufficient payload (model declined to judge)0.0150.022

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.357
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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