Finding FRiENDs: Creating a Community of Support for Early Career Academics
Bibliographic record
Abstract
Starting on an academic journey can be a stressful and isolating experience. Although some universities have formal mentoring structures to facilitate this transition for new faculty, these structures do not always provide the variety of supports that may be needed to navigate the complexities of transitioning to the world of academia. As we (the authors of this paper) began our academic journeys, we found ourselves searching for support that was not available within our institutions. By drawing on previous connections and building new connections to peers at other universities, we created an informal peer mentoring structure that has continued to support us through the early years of our careers in academia. In this paper we share our stories of the challenges we faced as early career academics, discuss the ways this informal peer mentoring community provided support for us at the beginnings of our academic journeys, and offer advice for other early career academics seeking non-traditional forms of support along the academic career path.
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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 | 0.001 |
| 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 | 0.001 |
| 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".