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Record W4409656814 · doi:10.31219/osf.io/y569z_v1

Investigating journal peer review as scientific object of study: unabridged version – Part II

2017· preprint· en· W4409656814 on OpenAlexfundno aff
Joanne Gaudet

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsObject (grammar)PreprintComputer sciencePsychologyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

The main goal of this paper is to construct journal peer review as a scientific object of study based on historical research into the shaping of its structural properties. This paper is a second in a two-part series. Journal peer review performed in the natural sciences has been an object of study since at least 1830. Researchers mostly implicitly frame it as a rational system with expectations of rational decision-making. This in spite of research debunking rationality where journal peer review can yield low inter-rater reliability, be purportedly biased and conservative, and cannot readily detect fraud or misconduct. Furthermore, journal peer review is consistently presented as a process started in 1665 at the first journals and as holding a gatekeeper function for quality science. In contrast, socio-historical research portrays journal peer review as emulating previous social processes regulating what is to be considered as scientific knowledge (or not) (cf., inquisition, censorship) and early learned societies as engaged in peer review with a legal obligation under censorship. However, to date few researchers have sought to investigate journal peer review beyond a pre-constructed process or self-evident object of study. I construct journal peer review as a scientific object of study with key analytical dimensions: structural properties. I use the concept of social form to capture how individuals relate around a particular content. For the social form of ‘boundary judgement’, content refers to decisions from the judgement of scientific written texts held to account to an overarching knowledge system. Given its roots in censorship with its function of bounding science, I frame journal peer review as following precursor forms of inquisition and censorship. The main implication from insights in the paper is that structural properties in boundary judgement social forms are understood as dynamic when looked at through a historical lens.

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)0.047
metaresearch head score (Gemma)0.217
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.217
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0060.030
Scholarly communication0.0290.017
Open science0.0030.009
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0140.004

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 head0.760
GPT teacher head0.627
Teacher spread0.133 · 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 designQualitative
DomainEvaluation
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

Citations0
Published2017
Admission routes1
Has abstractyes

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