The Oral Examination One More Hurdle to Go
Bibliographic record
Abstract
Abstract Congratulations! You have made it to the last stage of the ABCN certification process: the oral examination. As you may already know, this step consists of three separate, but equally important parts. Although each element emphasizes different approaches and content, they will all be used to judge your depth of clinical knowledge, neuropsychological skills, and your general clinical practice. The three parts of the exam are described in Box 5.1. Oral examinations are held twice a year, in May and October. In order to verify specific dates, you can either check the ABCN webpage or call the ABCN office (see Chapter 1 for contact information). When we wrote this book, the oral exams were held in Chicago and only in Chicago. This means that, unlike the written examination, you will not be able to take the orals at a conference when it comes near you. The odds are you will have to travel. Still, Chicago is centrally located and generally quite easy to fly to. As such, it is the most geographically accessible location available for the majority of the United States and Canada. So, rather than complaining that you have to travel to Chicago to take your orals, be thankful that ABCN did not decide to offer the examination in Sitka, Alaska.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.103 | 0.092 |
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 source (direct Gemma or distilled Codex), 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".