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Record W2791201767 · doi:10.2966/scrip.070310.421

Mapping The Coverage Of Neuroimaging Research

2010· article· en· W2791201767 on OpenAlexafffund
Timothy Caulfield, Christen Rachul, Amy Zarzeczny, Henrik Walter

Bibliographic record

VenueSCRIPTed A Journal of Law Technology & Society · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsNeuroimagingComputer scienceArtificial intelligencePsychologyNeuroscience

Abstract

fetched live from OpenAlex

The increasing popularity of neuroimaging studies among the research community in recent decades has also garnered interest from the media. But there is concern that coverage of controversial topics, such as the ability of fMRI to “read minds”, has sensationalised the neuroimaging field and led to mounting criticism and skepticism. In order to explore this phenomenon, we mapped the frequency and tone of research and review articles regarding fMRI published in the journals Science and Nature. We also examined the frequency and tone of the newspaper articles that reported the results of these research articles. The results indicate a distinct trend in the level of interest in neuroimaging studies, the topics of research, and the concomitant criticism over time. It appears that while more sensational research articles generate more media coverage, they also receive more criticism from within the scientific community. The results also provide tentative support for the existence of a classic “hype cycle” that may raise important questions about public perception and the longterm integrity of the neuroimaging field.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.081
GPT teacher head0.307
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2010
Admission routes2
Has abstractyes

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