The extent of academic spam within academic medicine
Notice bibliographique
Résumé
Background Researchers and physicians frequently receive unsolicited academic invitations from unknown sources, many of which are irrelevant to their area of expertise. These invitations often originate from questionable organizations. Despite growing awareness of this issue within academia over the past decade, there has been limited research into its prevalence and impact, particularly within the field of academic medicine. We sought to determine the prevalence and impact of academic spam among academic medical professionals at the university health network. Methods A literature review was conducted to evaluate the existing body of evidence on this topic. A two-phase qualitative survey was distributed to physicians at our institution. Over the course of 1 week, physicians submitted examples of academic spam they had received and completed a follow-up survey to assess the impact of these invitations. The survey responses were then analyzed qualitatively, and emerging themes were identified. Results A total of 549 emails not intercepted by the institutional spam filter from 15 participants were forwarded to the research team for an aggregate of 70 total days. The average number of spam emails received per week was 59, ranging from 1 to 30 emails per day with daily mean of 7 to 8 emails. Of 538 submissions deemed to be spam, 46.3% were notifications from journals, 21.8% were invitations to conferences, 7.8% were invitations to serve on an editorial board, 9.7% were newsletter alerts, 5.9% were invitations for webinars or courses, 5.4% were paid products or services, and 3.5% were other academic invitations or requests. A total of 12.8% of spam emails referenced a fee, ranging from waived to GBP $650. Only 13% of the spam collected mirrored the participants' academic interests. Data obtained from the institution's information technology team indicated that 75% of all incoming emails are blocked, illustrating that the true burden of academic spam may be up to four-fold greater than our survey showed. Conclusions Academic spam invitations were found to be a nuisance, often irrelevant to the recipient's area of research, and varied depending on the number of publications of the recipient. The volume and frequency of such invitations may lead to the overlooking or neglect of professionally relevant requests.
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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 | 0,023 | 0,116 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».