Assessing Formatting Accuracy of APA Style References: A Scoping Review
Notice bibliographique
Résumé
Objective – The objective of this scoping review is to synthesize the existing literature on the accuracy of formatting American Psychological Association (APA) Style references, with a focus on how accuracy has been defined and measured across studies. Specifically, the review aims to identify commonly reported formatting errors, evaluate the transparency and reproducibility of research methods, and assess whether standard assessment tools have been proposed or developed. Additionally, the review gathers the discipline and geographic location of study authors and examined how issues of diversity, equity, and inclusion (DEI) are addressed in this body of research. Methods – The review followed the JBI methodology for scoping reviews, with a registered protocol on the Open Science Framework. A comprehensive search strategy was executed in the following academic databases: Academic Search Complete, Business Source Complete, CINAHL Plus with Full-Text, Education Source Complete, LISTA, ProQuest Platform Search, and the Web of Science Core Collection. This was supplemented with Google and Google Scholar searches. Initial searches were conducted in May 2023 and updated in November 2024. Eligibility criteria included English-language studies that assessed APA Style formatting accuracy in reference list entries. Two independent reviewers conducted all phases of screening and data extraction, with discrepancies resolved through consensus or third-party adjudication. Citation searching was also employed, yielding additional studies. Data extracted included publication details, source types, accuracy measures, and identified biases. Results – Out of the included 32 studies, most were authored by researchers in Library Science and published in North America between 2006 and 2024. APA Manual editions from the 3rd to the 7th were represented. Reference sources most often came from student papers (41%), followed by article reference lists and databases. The most frequently analyzed source types were journal articles and books. Fourteen studies evaluated automated tools that create references, including tools embedded in databases, citation managers, and AI tools such as ChatGPT. Seventeen types of errors were pre-identified and nine additional error types were noted from the included studies. However, error classification terminology varied widely across studies, limiting comparability. While some studies used comprehensive checklists to assess accuracy, only a few tools were accessible, and no standardized, widely accepted assessment method emerged. Formatting accuracy was quantified using 64 different types of metrics, with inconsistent use of normalized measures. Only one study explicitly addressed a DEI-related issue—mis-formatting of names from non-Western cultures—highlighting an underexplored area of concern. Citation searching was notably effective in identifying studies not indexed in major databases. Conclusion – This review reveals a fragmented research landscape regarding how formatting accuracy of APA references is measured and described. There is no consensus on assessment methodology, terminology, or reporting metrics, making it difficult to benchmark or compare results across studies. The findings underscore the need for standardized, source-specific tools to assess formatting accuracy and call attention to the role of librarians and educators in addressing this gap. Additionally, more attention must be paid to equity considerations, particularly related to name formatting conventions. Consistent terminology, inclusive practices, and evidence based tools are essential for advancing citation literacy and supporting academic integrity.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,413 | 0,774 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,010 | 0,010 |
| Bibliométrie | 0,056 | 0,050 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,019 | 0,019 |
| Science ouverte | 0,008 | 0,011 |
| Intégrité de la recherche | 0,007 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».