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Record W33876983 · doi:10.3148/cjdpr-2021-005

L'imputazione soggettiva della colpa nella dottrina e giurisprudenza di lingua tedesca

2010· article· en· W33876983 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRivista italiana di diritto e procedura penale · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Despite the widespread use of statistical techniques in quantitative research, methodological flaws and inadequate statistical reporting persist. The objective of this study is to evaluate the quality of statistical reporting and procedures in all original, quantitative articles published in the <i>Canadian Journal of Dietetic Practice and Research</i> (CJDPR) from 2010 to 2019 using a checklist created by our research team. In total, 107 articles were independently evaluated by 2 raters. The hypothesis or objective(s) was clearly stated in 97.2% of the studies. Over half (51.4%) of the articles reported the study design and 57.9% adequately described the statistical techniques used. Only 21.2% of the studies that required a prestudy sample size calculation reported one. Of the 281 statistical tests conducted, 88.3% of them were correct. <i>P</i> values >0.05-0.10 were reported as "statistically significant" and/or a "trend" in 11.4% of studies. While this evaluation reveals both strengths and areas for improvement in the quality of statistical reporting in CJDPR, we encourage dietitians to pursue additional statistical training and/or seek the assistance of a statistician. Future research should consider validating this new checklist and using it to evaluate the statistical quality of studies published in other nutrition journals and disciplines.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.308
Teacher spread0.287 · 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