The extent of academic spam within academic medicine
Bibliographic record
Abstract
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. 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. 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. 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".