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Record W2475318605 · doi:10.1177/0306312716659373

Beams of particles and papers: How digital preprint archives shape authorship and credit

2016· preprint· en· W2475318605 on OpenAlexaff
Alessandro Delfanti

Bibliographic record

VenueSocial Studies of Science · 2016
Typepreprint
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInvisibilityPreprintReading (process)TemporalitiesPublishingComputer scienceScrutinyOrder (exchange)Space (punctuation)Media studiesWorld Wide WebSociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In high energy physics, scholarly papers circulate primarily through online preprint archives based on a centralized repository, arXiv, that physicists simply refer to as 'the archive'. The archive is not just a tool for preservation and memory but also a space of flows where written objects are detected and their authors made available for scrutiny. In this article, I analyze the reading and publishing practices of two subsets of high energy physicists: theorists and experimentalists. In order to be recognized as legitimate and productive members of their community, they need to abide by the temporalities and authorial practices structured by the archive. Theorists live in a state of accelerated time that shapes their reading and publishing practices around precise cycles. Experimentalists turn to tactics that allow them to circumvent the slowed-down time and invisibility they experience as members of large collaborations. As digital platforms for the exchange of scholarly articles emerge in other fields, high energy physics could help shed light on general transformations of contemporary scholarly communication systems.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchScholarly communication
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearchScience and technology studiesScholarly communication
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.042
metaresearch head score (Gemma)0.302
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.302
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.015
Science and technology studies0.0100.016
Scholarly communication0.0390.038
Open science0.0030.013
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0320.007

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 head0.157
GPT teacher head0.384
Teacher spread0.227 · 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

Labeled directly by 2 models reading the full record.

MetaresearchScholarly communicationScience and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainEvaluation · Incentives
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
Published2016
Admission routes1
Has abstractyes

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