An approach to conference selection and evaluation: advice to avoid “predatory” conferences
Bibliographic record
Abstract
With an ever increasing number of academic conferences being offered and a growing concern regarding the appearance of “predatory conferencing”, the optimal use of one’s “conference time” has become complicated. It is particularly important for medical students and residents, due to both limited time and resources, to choose conferences to achieve the most benefit for their academic careers, however little information is available to provide guidance. We gathered current practices and perceptions from a wide number of faculty and residents to provide insights into their decision making process for conference evaluation and selection. We surveyed using an online program (SurveyMonkey®) 150 faculty members and medical residents at University of Calgary requesting them to rank factors, which may be useful in their conference selection. We also evaluated both faculty and resident’s knowledge of and exposure to presumed “predatory” science. Responses were anonymized and collated. The most important factors in selecting a conference(s) for residents is being focused on their area of interest, having an opportunity to present work, having well known respected plenary speakers, networking potential and being recommended by colleagues. Residents placed more importance on cost, location and time of year of the conference than faculty. Faculty placed more emphasis on networking potential, sponsoring organization, obtaining continuing professional development credits and the likelihood that cutting edge research will be presented. Most faculty (71%) had received correspondence from presumed “predatory” publishers or conferences, however only 56% of residents had ever either heard of or had education about “predatory science”. Experienced faculty need to provide advice and mentorship to trainees on conference value. In the absence of any formal tool for assessing conference quality their proactive guidance remains critical for medical students and residents. There is a significant lack of awareness and education for both trainees and some faculty around “predatory” publications and conferencing making them particularly vulnerable to misusing time and resources. Improved knowledge, education and new metrics are required to safeguard the academic community in this new era of “predatory academia”.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.265 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".