Strategies for Getting Promoted in a Difficult Granting Environment
Bibliographic record
Abstract
Research, teaching and service. These are the areas that we all know we need to excel in to get promoted. We can easily control our teaching quality and service levels, but it's the research component where many of us are currently facing our greatest challenges. We're all too aware that research costs are escalating, national research funds are dwindling and those funds are becoming more targeted than ever before. So how can we get enough money to do our research and get the quality publications needed for promotion? In this session I will discuss some of the unconventional strategies I have used to supplement funds for my labs, international collaborations that are funneling money to my research program and how these tactics have helped me develop a strong internationally recognized research component and (thus far) unimpeded promotion as a faculty member. I will also point out the key steps that I took throughout my career that have helped in my career progression and how I've overcome obstacles that we all deal with as faculty members.
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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 | 0.075 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.037 | 0.025 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".