Presented to the Second Annual Mentoring Conference The Association of Professional Engineers, Geologists and Geophysicists of Alberta
Bibliographic record
Abstract
I suspected that there might be something wrong with this picture – you sitting in the audience and me standing up here and after I reviewed the mentoring section on the APEGGA website and read the mentoring guidelines (APEGGA, 2000) that was confirmed. I am sure that the expertise resides where you are and that I am going to learn a lot from today’s program. Open almost any book on recruiting, retaining, and advancing women in science, engineering, and technology these days and mentorship is mentioned. In a recent report from the National Research Council of the National Academies (2006) mentoring is noted as a retention strategy at all levels (undergraduate, graduate, and postdoctoral), an advancement strategy for women faculty, and a strategy for advancing women to executive positions. Boyle and Boice (1998) stated that “Mentoring may be the most important variable related to academic and career success for graduate students ” (p. 90). Are you curious about the basis for such claims? I know that I am, so when I received this invitation in June I began to explore that issue further and this morning I will be trying to answer the following questions: Is mentoring the flavour of the month? Are mentoring programs the panacea? (Good for what ails you). What do we know about the effectiveness of such programs?
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.
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 | 0.000 |
| 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 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".