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Record W1996867325 · doi:10.3138/jsp.46.1.004

From Book Publishers to Authors: Information Transparency in Web Sites

2014· article· en· W1996867325 on OpenAlexfundvenueno aff
Elea Giménez‐Toledo, Sylvia Fernández-Gómez, Carlos Miguel Tejada Artigas, Jorge Mañana-RodrÍquez

Bibliographic record

VenueJournal of Scholarly Publishing · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychology Research and Bibliometrics
Canadian institutionsnot available
FundersConsejo Superior de Investigaciones CientíficasUniversity of Toronto
KeywordsPublishingTransparency (behavior)Scholarly communicationLibrary scienceRelation (database)World Wide WebPolitical scienceSociologyComputer scienceLaw

Abstract

fetched live from OpenAlex

The publishing processes and standards in scholarly journals are much better known than those of the publishers of scholarly books. Since scholarly books are key channels of communication and academic assessment in the humanities and social sciences, information provided by publishers concerning their publishing processes is very important both for authors and panelists (at funding and evaluation agencies). This article focuses on the analysis of the transparency of publishers in relation to the information they offer to authors. The main objective is to identify and analyze the publishing practices of two hundred scholarly book publishers of social sciences and humanities with respect to the information that they provide on their Web sites about their publishing processes. A lack of information on these Web sites is the main finding of the study. Among Spanish publishers, only 11.2 per cent explicitly state that they have a review system by experts. At the international level, the situation improves, but the shortcomings are still evident. Some guidelines for publishers are outlined and proposed.

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
gemmaScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
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)0.046
metaresearch head score (Gemma)0.230
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.230
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.016
Science and technology studies0.0030.004
Scholarly communication0.0160.018
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.057
GPT teacher head0.349
Teacher spread0.293 · 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.

Scholarly communication

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

Study designNot applicable · Observational
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

Citations3
Published2014
Admission routes2
Has abstractyes

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