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Record W4413183986 · doi:10.1093/scipol/scaf034

Stockholm science: the syndrome, not the city!!!

2025· article· en· W4413183986 on OpenAlexaff
Sávio Torres de Farías

Bibliographic record

VenueScience and Public Policy · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Abstract The development of scientific knowledge is crucial for the modernization of society, but the way this knowledge is disseminated also has a significant impact. Since the creation of the first scientific societies and journals, the traditional publishing model has been largely dominated by publishers, restricting access to scientific findings. The open-access model, introduced in the 1990s, initially aimed to democratize access but has gradually transformed into a profitable system based on article processing charges, creating a cycle of exclusion. Scientists, especially young ones or those from underfunded groups, face significant financial barriers to publishing in prestigious journals. The dependence on these models is creating hierarchical divisions in science, leading to a system that, despite its democratic origins, perpetuates inequalities and limits innovation.

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.996
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0080.029
Scholarly communication0.0190.010
Open science0.0010.013
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0170.006

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 head0.510
GPT teacher head0.600
Teacher spread0.089 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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