Preparing for the academic job market: An interactive panel for doctoral students [a panel proposal]
Bibliographic record
Abstract
Abstract The transition from doctoral student to assistant professor can be a challenging one for many students. The process is unfamiliar for many and presents unanticipated challenges and opportunities. The function of this panel is to provide an interactive platform for faculty members at all stages of their careers to provide advice and input for doctoral students nearing the completion of their doctoral work. This panel will provide valuable insight on finishing the dissertation, going on the job market, and beginning an academic career. The format will allow for participants to ask questions anonymously, questions that may be embarrassing to ask. The seven panelists represent all stages of the academic career: two assistant professors, three associate professors (including two associate deans), and two full professors (including one interim dean and one dean). The participants come from seven different institutions, representing two countries (U.S. and Canada). The panel will be of greatest use to those doctoral students at the end of their doctoral program, but may also be of interest to doctoral students beginning their doctoral work and new assistant professors.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".