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Record W2995595645 · doi:10.5194/gc-3-89-2020

The benefits to climate science of including early-career scientists as reviewers

2020· article· en· W2995595645 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeoscience Communication · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversité LavalUniversité du Québec à Rimouski
FundersAlexander von Humboldt-Stiftung
KeywordsPeer reviewPublic relationsFeelingWorkforceProcess (computing)Set (abstract data type)PsychologyPolitical scienceMedical educationEngineering ethicsMedicineComputer scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Abstract. Early-career scientists (ECSs) are a large part of the workforce in science. While they produce new scientific knowledge that they share in publications, they are rarely invited to participate in the peer-review process. Barriers to the participation of ECSs as peer reviewers include, among other things, their lack of visibility to editors, inexperience in the review process and lack of confidence in their scientific knowledge. Participation of ECSs in group reviews, e.g. for regional or global assessment reports, provides an opportunity for ECSs to advance their skill set and to contribute to policy-relevant products. Here, we present the outcomes of a group peer review of the First Order Draft of the Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). Overall, PhD students spent more time on the review than those further advanced in their careers and provided a similar proportion of substantive comments. After the review, participants reported feeling more confident in their skills, and 86 % were interested in reviewing individually. By soliciting and including ECSs in the peer-review process, the scientific community would not only reduce the burden carried by more established scientists but also permit their successors to develop important professional skills relevant to advancing climate science and influencing policy.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0050.002
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.511
GPT teacher head0.467
Teacher spread0.043 · 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