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Record W2766372071 · doi:10.1002/pra2.2017.14505401100

Human dimensions of peer review in information science

2017· article· en· W2766372071 on OpenAlexaff
Keren Dali, Paul T. Jaeger, Lilith Lee

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAppealPublicationPrivilege (computing)Subject (documents)Argument (complex analysis)PublishingSociologyPeer reviewPublic relationsEngineering ethicsPresentation (obstetrics)PsychologyPolitical scienceComputer scienceLibrary scienceEngineeringLawMedicine

Abstract

fetched live from OpenAlex

ABSTRACT While peer review is a popular research subject in academia, including information science, most sources focus on the mechanics, benefits and shortcomings of the process. In departure from this trend, this poster will address the human dimension of peer review and its role as a community building instrument and a locus of relationship formation. The poster will look at how “negligent and unscrupulous reviewing can detrimentally affect” “a sense of community” (Dali & Jaeger, n.d.) and support this argument through a new analytical framework based on humanistic pedagogy. It will address the role of peer review in the changed information science landscape; present a positive outlook on peer review “as a privilege and an unmatched academic opportunity”; “examine in detail the elements of helpful and unhelpful reviews”; and provide “advice to authors on how to respond to reviews, especially the unhelpful ones” (Dali & Jaeger, n.d.). The poster should appeal not only to junior faculty and PhD students, but also to practitioners who aspire to publish and experienced authors involved in scholarly communication and grant reviewing.

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 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)0.009
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.010
Open science0.0010.000
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 head0.020
GPT teacher head0.355
Teacher spread0.335 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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