Preparing for the academic job market: An interactive panel for doctoral students
Bibliographic record
Abstract
Abstract This proposal expands on the basic format of the successful interactive doctoral student panel sponsored by SIG/ED at the 2011 ASIS&T annual meeting. The 2012 panel will feature several new panelists and a discussion of alternative career paths to the traditional job market, such as postdoctoral 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, weighing post‐doctoral opportunities, entering the job market, and beginning an academic career. The format will allow participants to ask questions anonymously that may otherwise be embarrassing to ask. The seven panelists represent all stages of an academic career: three assistant professors, two associate professors (including an associate dean), and two full professors (including one dean). The participants come from six different institutions and represent 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, as proven in 2011, will also be of interest to doctoral students beginning their doctoral work and new assistant professors.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
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".