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Record W3015382945 · doi:10.1108/cc-12-2019-0046

Analysis of the usage and diversity of grey literature in addiction research: a study

2020· article· en· W3015382945 on OpenAlexaboutno aff
Rishabh Shrivastava, Preeti Mahajan

Bibliographic record

VenueCollection and Curation · 2020
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsScopusGrey literatureAddictionPublishingPsychologyPolitical scienceMEDLINE

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is twofold. First, the study shall evaluate the extent of usage of grey literature and its different types of addiction research. The second purpose of the research is to analyze the extent of usage of reports such as research reports, survey reports, data reports, etc. As the reports are produced in general by various organizations and can be accessed by not only academicians but also the general public, they play an important role in the dissemination of research to the people. Therefore, the study endeavored to identify the major organizations that are involved in the publishing of research reports in the field of addiction. Design/methodology/approach Scopus database was used for the purpose of collecting the data. References in the reference lists of the articles published in 2018 in the journal Psychology of Addictive Behaviors of the American Psychological Association were collected. Scopus indexes the references of the papers in two different categories, namely, indexed in scopus/scopus references and reference lists. They were then categorized as grey literature and non-grey literature. Further, reports were searched manually so that their producers/authors can be found and categorized according to the organizations. Findings The study found that grey literature comprises a very small proportion of citations in addiction research (just approximately 5 per cent). This suggests that the improper indexing and bibliographic control of grey literature may be one of the reasons behind the low numbers. Reports comprised the largest proportion of the grey literature cited in addiction research, followed by software documentation, unpublished manuscripts, guidebooks, handbooks, manuals, websites, government publications, etc. The reports of the US Department of Health and Human Services comprised the maximum citations in the reports category because of the reports produced by organizations like Substance Abuse and Mental Health Services Administration (SAMHSA) which comprised 17.59 per cent of the total reports. National Institute of Health (USA) and Centers for Disease Control and Prevention and others. Other than the reports of the organizations of the USA, the reports published by the organizations of Canada, Australia, UK, New Zealand and one European Body were also cited by the articles of the journal. Practical implications The research focuses on the use of grey literature in addiction research. The findings of the study indicate very low citations to grey literature in addiction research. This reinforces the need for a separate worldwide information retrieval system for grey literature for researchers to conduct systematic reviews. Originality/value Very few studies have been conducted on the use of grey literature and hardly any research focuses on the use of grey literature in addiction research. The study goes one step further and identifies major organizations that are involved in the production of research reports in the field so that their reports can be properly indexed.

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)0.049
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.187
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0470.047
Science and technology studies0.0020.004
Scholarly communication0.0080.006
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.321
Teacher spread0.242 · 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 designObservational
DomainReporting
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

Citations5
Published2020
Admission routes1
Has abstractyes

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