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Record W2599222526 · doi:10.1371/journal.pcbi.1005373

Ten simple rules for short and swift presentations

2017· editorial· en· W2599222526 on OpenAlexaff
Christopher J. Lortie

Bibliographic record

VenuePLoS Computational Biology · 2017
Typeeditorial
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsYork University
Fundersnot available
KeywordsSwiftSimple (philosophy)Computer scienceComputational biologyBiologyProgramming languagePhilosophy

Abstract

fetched live from OpenAlex

Scientific communication is an independent research domain and has become a fundamental component of most scientific discourse and all public outreach.It now comprises a set of critical activities for many research programs [1,2], including those that directly influence global and human health [3].Scientific communication has evolved because it does not have to happen only at the final stages of a research endeavor but can be used to engage the public to fund the research (https://scifundchallenge.org), participate in the data collection (http://www. audubon.org/conservation/science/christmas-bird-count),share or crowd source the code and analyses (https://github.com),and process the evidence (https://www.zooniverse.org).Unfortunately, scientific progress in some fields such as climate change has outpaced our capacity to effectively communicate and contextualize findings for the public [4].To mitigate this shortcoming, resources specific to scientists have been developed [5][6][7][8].Boot-camp training workshops are now also offered (i.e., American Institute of Biological Sciences [AIBS]), and discussion of how academics use brief communications, such as social media tools, is present within the primary research literature [9][10][11].An interesting related opportunity has emerged that, in some respects, bridges the gap between lengthy, detailed presentations of scientific findings and "sound bites" such as headlines or short press releases appropriate for media reporting: very short, swift presentations.Admittedly, these talks are in essence sound bites, too, but with more depth and thus requiring special consideration in terms of how to best leverage their potential [12].These shorter presentations are commonly directed both to peers at scientific conferences and to the general public at in-person events and online.This format is particularly suited to online dissemination and sharing through YouTube, with most major scientific institutions and organizations administering channels of curated content.Many major scientific conventions include offerings of rapid-fire format talks-at first to communicate meta-science but now also to share primary research findings.The specific guidelines vary, but the slide deck is often limited by a set number of slides, or the presentation is limited by very strict, short time constraints (such as found with lightning talks).In addition, the slides can be set to rapidly autoadvance, for instance, with PechaKucha presentations.These presentation formats are also organized into open, public series and feature involvement from experts in many disciplines on numerous topics, including science.Succinct prose is thus a critical element in communicating science using these presentation formats.On a cautionary note, reducing much longer talks to these shorter formats is likely not the most effective strategy because shorter total presentation times coupled with rapid pacing can dramatically influence the scope and depth of the material.Best practices for scientific communication certainly apply to these talks, but specific strategies are nonetheless needed.For instance, as a general rule-of-thumb, talks prepared for a more general public audience should emphasize the

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)0.059
metaresearch head score (Gemma)0.227
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.227
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0090.008
Scholarly communication0.0280.031
Open science0.0060.014
Research integrity0.0130.012
Insufficient payload (model declined to judge)0.0970.103

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 head0.118
GPT teacher head0.482
Teacher spread0.364 · 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
DomainReporting
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

Citations19
Published2017
Admission routes1
Has abstractyes

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