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Record W2059793833 · doi:10.1093/scipol/scs122

[Introduction to set of three reviews on Standards: Recipes for Reality (Busch, 2011), and Scientists and the Regulation of Risk: Standardising Control (Demortain, 2011).]

2012· article· en· W2059793833 on OpenAlex
Meaghan Brierley, C. H. Langford

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

VenueScience and Public Policy · 2012
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPopularitySet (abstract data type)Perspective (graphical)Control (management)Library scienceComputer sciencePolitical scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

In the autumn of 2011 Science and Public Policy sent out a call for reviews of two books recently published on standardisation: Standards: Recipes for Reality (Busch, 2011), and Scientists and the Regulation of Risk: Standardising Control (Demortain, 2011). The popularity of the titles was such that when Fern Wickson suggested a review series we took advantage of her offer to review both books. Tolu Odumosu and Joel D'Silva then agreed to write reviews of each separately. Published here are the results of the experiment. We believe the three-reviews-of-two-books experiment was successful. The reviewers wrote independently, and upon completion read the others’ contributions. However, all the reviewers retained their original approach. The three articles undulate across the topics of the books—ideas introduced by one author may be furthered by another, while at the same time, each approaches from a unique perspective. The group provides as thorough an understanding of the books’ main ideas as can be attained in this format.

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.

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
gemmaScience and technology studies
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptScience and technology studies
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.334
Teacher spread0.308 · 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