Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mentoring is routinely used as a tool to facilitate acquisition of skills by new professionals in fields like medicine, nursing, surgery, and business. While mentoring has been proposed as an effective strategy for knowledge and skills transfer in biostatistics and related fields, there is still much to be done to facilitate adoption by stakeholders, including academia and employers of biostatisticians. This is especially troubling given that biostatisticians play a key role in the success or otherwise of clinical research conducted for evidence-based decisions. In this paper, we offer suggestions on how mentoring can be applied in practice to advance the statistical training of future biostatisticians. In particular, we propose steps that academic statistics departments, professional statistical societies, and statistics organizations can take to advance the mentoring of young biostatisticians. Our suggestions also cover what mentors and mentees can do to facilitate a successful mentoring relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it