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Enregistrement W2904625289 · doi:10.1093/beheco/ary163

Systematic evidence synthesis as part of a larger process: a response to comments on Berger-Tal et al.

2018· article· en· W2904625289 sur OpenAlexaff
Oded Berger‐Tal, Alison L. Greggor, Biljana Macura, Carrie Ann Adams, Arden Blumenthal, Amos Bouskila, Ulrika Candolin, Carolina Doran, Esteban Fernández‐Juricic, Kiyoko M. Gotanda, Catherine J. Price, Breanna J. Putman, Michal Segoli, Lysanne Snijders, Bob B. M. Wong, Daniel T. Blumstein

Notice bibliographique

RevueBehavioral Ecology · 2018
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueMeta-analysis and systematic reviews
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésBiologyProcess (computing)EpistemologyComputer scienceProgramming language

Résumé

récupéré en direct d'OpenAlex

We are encouraged that the prospect of generating systematic reviews and maps (Berger-Tal et al. 2019) has stirred enthusiasm among our peers. The resulting discussion brings up a number of valid points that share a vision for a field with greater internal rigor and external impact. The quality of a review relies heavily on the quality, topical diversity, and open-access availability of the primary literature. We agree with Nakagawa and Lagisz (2019) that promoting reporting standards for experimental studies should be a priority of journals, scientists, and educators. Not only will better reporting help improve the basic level of science in the field, but it will also increase the likelihood that studies can be used as evidence in other contexts (see the EQUATOR Network, www.equator-network.org, for a good example from the field of health research). Assessing the value and the rigor of the science involved in any synthesis is extremely important. We agree with Stewart and Ward (2019) that the potential detrimental weight of a small group of “experts” in the evidence base needs to be considered when investigating potential sources of bias. The ability of scientists to analyze the quality of the science itself is key in making these decisions, and new tools are emerging to aid this process (Nakagawa et al. 2019). Review authors should not be immune from the scrutiny of bias themselves, especially if developing prescriptive evaluations (Stewart and Ward 2019), which is why peer review and a standard reporting format is vital for reducing these biases. Our focus on systematic reviews that adhere to the strict Collaboration for Environmental Evidence (CEE) guidelines does not eliminate the value of other types of literature syntheses. We agree with Nakagawa and Lagisz (2019) that reviews can take various forms that follow the same principles of transparency, repeatability, and rigor. How rigorous or thorough the search is (i.e., how many databases or languages are searched) will depend on the question being asked, the urgency of the situation, and the resources of the team involved. Of course, the more comprehensive the search is, the better it will be able to inform policy and practice. Regardless of the scope of the effort, the ultimate purpose of any review should be considered when the format is chosen, and the methods must communicate biases that can arise from less thorough search efforts. We agree with Griffin and Hayward (2019) that systematic reviews offer opportunities for engaging with stakeholders in a productive and meaningful way but that making those connections initially can be a challenge. As it becomes more of a priority for our fields to interact and communicate, our hope is that connections will be easier to forge and more of a priority to maintain. The idea of a central registry to facilitate communication between scientists and managers is certainly an exciting one. We applaud and encourage all efforts to make those connections easier. In addition, we agree with Sih et al. (2019) that formulating the systematic review question is key and that these questions should be rooted, whenever possible, in existing or emerging theoretical and conceptual frameworks. Without an understanding of mechanism, the ability of evidence to generalize across species or contexts is greatly diminished. However, stakeholder engagement still remains a crucial part of the question formulation process to ensure that the review question is not only useful, but also relevant. By bringing scientists and other stakeholders together in the question formulating process, systematic reviews can help bridge the much discussed gap between academia and the real world. Part of facilitating communication with stakeholders involves transforming the science into a digestible format. For some stakeholders, this may be the narrative synthesis alone (which, as Griffin and Hayward (2019) bring up, can be a challenge to craft in an unbiased way). For other stakeholders, the act of communicating results may be better done in person (as Caro 2019 notes), at workshops, conferences, meetings of species’ recovery groups, or via other media platforms. The fact that practitioners do not often have time to read primary literature (Caro 2019) is a major reason for engaging with the full systematic review process, not a drawback of the process itself. As scientists, it is our job to synthesize what conclusions can be drawn from the evidence base and tailor their presentation to the intended audience. By combating evidence complacency outside of our scientific bubble, we can increase the likelihood that it will be used (Walsh et al. 2014), infinitely more so than if we do nothing. Overall, the pursuit of systematic reviews is not an easy task, as several authors note. Covering highly heterogeneous fields is a challenge, but one that we hope scientists will meet. As Griffin and Hayward (2019) suggest, despite the effort involved, systematic reviews should offer a worthwhile use of academics’ time if they want their science to have meaningful impact.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,294
score de la tête « metaresearch » (Gemma)0,623
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,706
Score d'incertitude au seuil0,871

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2940,623
Méta-épidémiologie (sens strict)0,0030,004
Méta-épidémiologie (sens large)0,0060,008
Bibliométrie0,0050,007
Études des sciences et des technologies0,0120,020
Communication savante0,0170,023
Science ouverte0,0120,017
Intégrité de la recherche0,0760,114
Charge utile insuffisante (le modèle a refusé de juger)0,0080,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,667
Tête enseignante GPT0,585
Écart entre enseignants0,081 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
DomaineMéthodes
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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